WO2026030757A1 - Wearable-based health system for detecting stress and recovery - Google Patents

Abstract

A computing device performs physiological state monitoring. The devices receives first biometric data from a body-worn sensing device, determines a first heart rate variability (HRV) of the user, and sets a daily threshold distinguishing activated and recovery states based on the first HRV. Subsequent biometric data is received throughout the day to determine a second HRV. The user's current state, an activated or recovery state, is identified by comparing the second HRV to the daily threshold. An indication of the user's state is output via a human machine interface (HMI).

Description

WEARABLE-BASED HEALTH SYSTEM FOR DETECTING STRESS AND RECOVERY

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority to U.S. Provisional Patent Application No. 63/678,962, filed August 2, 2024, U.S. Provisional Patent Application No. 63/709,579, filed October 21 , 2024, and U.S. Provisional Patent Application No. 63/709,694, filed October 21 , 2024, each of which is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

[0002] The present disclosure is directed to a wearable sensing device for measuring one or biometric features of the wearer. The systems and methods described herein focus on devices that sense, measure, transmit, and present various data, including, but not limited to data regarding physiological conditions within and/or in proximity to a wearer’s body, including a vital sign, biodata and temperature within an artificially created cavity (created cavity temperature or “CCT”) in a wearer’s body.

BACKGROUND

[0003] Wearable devices are electronics designed to be worn on the body. They can include watches, arm bands, head mounted displays (HMDs), smart glasses, or other accessories. These devices are equipped with sensors that can sense various types of data, including temperature, movement, and other physiological signals.

[0004] By measuring these parameters, wearables can provide insights into an individual’s general health and wellness. The sensor data can be used to determine activity levels, monitor sleep, detect changes in vital signs, and help users better understand their physical state. Over time, this information can support users in improving health, lifestyle, and daily routines. BRIEF SUMMARY

[0005] Aspects and advantages of embodiments of the present disclosure will be set forth in part in the following description, or can be learned from the description, or can be learned through practice of the embodiments.

[0006] In an aspect, a method is performed by a computing device, comprising: receiving first biometric data from a body-worn sensing device worn by a user, the body worn sensing device comprising at least a photoplethysmography (PPG) sensor; determining, based on the first biometric data, a first heart rate variability (HRV) of the user; setting, based on at least the first biometric data, a daily threshold associated with a first state (e.g., an activated state) and second state (e.g., a recovery state) of the user for a day; receiving subsequent biometric data from the body-worn sensing device during the day; determining, based on the subsequent biometric data, a second HRV of the user; determining that the user is in the first state or the second state based on the second HRV and the daily threshold; and outputting through a human machine interface (HMI), an indication that the user is in the first state or the second state.

[0007] In an embodiment, the first biometric data is collected within a threshold period of time from the user waking from sleep, and setting the daily threshold comprises setting the daily threshold as the first HRV or as the first HRV with one or more adjustments. In an embodiment, the one or more adjustments to the first HRV comprises an increase or decrease to the first HRV based on a sleep quality metric of the user. In an embodiment, the one or more adjustments to the first HRV comprises an increase or decrease to the first HRV based on a historical HRV of the user, an average HRV from a general population, or both. In an embodiment, the one or more adjustments to the first HRV comprises an increase or decrease to the first HRV based on an ambient temperature from the first biometric data, a user temperature from the first biometric data, or both.

[0008] In an embodiment, the method further comprises increasing or decreasing the daily threshold during the day in response to one or more of: detected activity of the user, the subsequent biometric data, a time of the day, an ambient or user body temperature, a historical behavior of the user, or a location of the user.

[0009] In an embodiment, the HMI comprises a display, and outputting the indication comprises generating and displaying a graphical indication of one or more transitions between the first state and the second state of the user during the day. In an embodiment, the method further comprises outputting, through the HMI, a notification to transition to or maintain the second state, based on the second HRV of the user. In an embodiment, outputting the indication to the user comprises determining a time of the day, and in response to the time of the day being within a first range, refraining from outputting the indication to the user.

[0010] In an embodiment, the setting of the daily threshold, the first HRV, or the second HRV, are performed by applying one or more machine learning models. For example, a machine learning model may be trained with labeled training data sets to output a target daily threshold, or HRV based on the biometric data.

[0011] In an embodiment, receiving the subsequent biometric data, performing the second HRV, and determining whether the second HRV satisfies the daily threshold is performed at periodic times, and a machine learning model is applied to update values of the second HRV or the daily threshold between the periodic times.

[0012] In an embodiment, the body-worn sensing device comprises one or more earrings, each comprising one or more of: a temperature sensor, an accelerometer, or a gyroscope. In an embodiment, the temperature sensor comprises a created cavity temperature (CCT) sensor, an ambient temperature sensor, or both.

[0013] In an embodiment, determining the first HRV based on the first biometric data comprises determining RR intervals in the first biometric data and determining a variation of the RR intervals over a period of time. In an embodiment, the variation is determined by applying a Root Mean Square of Successive Differences (RMSSD) or a Standard Deviation of NN intervals (SDNN) of the RR intervals over the period of time.

[0014] In an aspect, a computing device comprises one or more processors, coupled to memory storing instructions that, when executed by the one or more processors, cause the computing device to perform the method described above.

[0015] In an aspect, a computer-readable storage medium stores instructions that, when executed by one or more processors of a computing device, cause the computing device to perform the method described above.

[0016] In an aspect, a body-worn sensing device comprises: a transmitter, communicatively coupled to a computing device; one or more sensors that capture biometric data of a user, the one or more sensors including at least a photoplethysmography (PPG) sensor; and a processor, configured to perform operations comprising: transmitting, through the transmitter, first biometric data to the computing device, wherein the computing device is configured to determine, based on the first biometric data, a first heart rate variability (HRV) of the user, and to set, based on at least the first biometric data, a daily threshold associated with a first state (e.g., activated state) and a second state (e.g., recovery state) of the user for a day; and transmitting, through the transmitter, subsequent biometric data to the computing device during the day, wherein the computing device is further configured to determine, based on the subsequent biometric data, a second HRV of the user, to determine that the user is in the first state or the second state based on the second HRV and the daily threshold, and to output through a human machine interface (HMI), an indication that the user is in the first state or the second state.

[0017] In an embodiment, the body-worn sensing device comprises a first earring and a second earring. In an embodiment, the one or more sensors comprises a created cavity temperature (CCT) sensor that is thermally coupled to a post of the first earring and of the second earring. In an embodiment, the one or more sensors comprises an ambient temperature sensor that is not thermally coupled to the post. In an embodiment, the one or more sensors further comprise an accelerometer, a gyroscope, or both.

[0018] In an embodiment, a system comprises a body-worn sensing device and a computing device, which are communicatively coupled and perform the operations described above.

[0019] Some additional aspects below relate to a combined machine-learning model approach.

[0020] In one aspect of the present disclosure, a computing system is provided. The computing system includes a first machine-learned model trained to identify one or more phases of a menstrual cycle of a wearer of a wearable sensing device, wherein the first machine-learned model uses training data obtained from a plurality of individuals that may or may not include the wearer; a second machine-learned model trained to identify the one or more phases of the menstrual cycle of the wearer of the wearable sensing device, wherein the second machine-learned model uses training data obtained only from the wearer; one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include obtaining biometric parameter data from the wearer as measured via the wearable sensing device; inputting the biometric parameter data into the first machine-learned model for a first period of time, the first period of time being sufficient for an adequate amount of biometric parameter data from the wearer to be obtained by the wearable sensing device to be used as the training data to train the second machine-learned model; receiving, as an output of the first machine-learned model, the identification of the one or more phases of the menstrual cycle of the wearer; after the first period of time has elapsed, inputting the biometric parameter data obtained by the wearable sensing device into the second machine-learned model; and receiving, as an output of the second machine-learned model, the identification of the one or more phases of the menstrual cycle of the wearer.

[0021] In another aspect, the first machine-learned model is not used after the first period of time has elapsed so that only the second-machine learned model is used to identify the one or more phases of the menstrual cycle of the wearer after the first period of time has elapsed using biometric parameter data that is continuously input into the second machine-learned model.

[0022] In yet another aspect, the first period of time can include at least one complete menstrual cycle.

[0023] In still another aspect, the one or more phases of the menstrual cycle can include a menstruation phase, a follicular phase, an ovulation phase, and a luteal phase.

[0024] In an additional aspect, the wearable sensing device can include a first wearable sensing device and a second wearable sensing device, wherein the first wearable sensing device and the second wearable sensing device can each be inserted into a created cavity of the wearer. The first wearable sensing device and the second wearable sensing device can measure the one or more biometric data parameters simultaneously, or the first wearable sensing device can measure the one or more biometric data parameters while the second wearable sensing device is not activated and vice versa, such that the system toggles between wearable sensing devices.

[0025] In one more aspect, the one or more biometric data parameters can include created cavity temperature, SpCh, active energy expenditure, resting energy expenditure, total energy expenditure, sleep metrics, heart rate, heart rate variability, physical activity, or a combination thereof. [0026] In another aspect, the first machine-learned model, the second machine-learned model, or both can include one or more of a deep artificial neural network, a transformer network with attention mechanisms, a support vector machine, a decision tree, or a linear model. The deep artificial neural networks can include architectures that employ attention algorithms, such as self-attention and multi-head attention mechanisms, to enhance pattern recognition and feature extraction from sensor data.

[0027] In still another aspect, data corresponding with the one or more biometric data parameters and information related to the identification of the one of more phases of the menstrual cycle of the wearer can be input into a third machine-learned model, wherein the third machine-learned model outputs predictions related to the wearer’s physical health, mental health, stress level, readiness, restorative shift, resilience, or a combination thereof. Further, the third machine-learned model can include one or more of a large language model or a generative Al model.

[0028] In yet another aspect, data corresponding with the one or more biometric data parameters and information related to the identification of the one of more phases of the menstrual cycle of the wearer input into a fourth machine-learned model, wherein the fourth machine-learned model outputs recommendations to improve the wearer’s physical health, mental health, stress level, readiness, restorative shift, resilience, or a combination thereof. Further, the fourth machine-learned model can include one or more of a large language model or a generative Al model.

[0029] In one more aspect of the present disclosure, a method is provided that includes obtaining, by a computing system, biometric parameter data from a wearer of a wearable sensing device as measured via the wearable sensing device; inputting, by the computing system, the biometric parameter data into a first machine-learned model for a first period of time, the first period of time being sufficient for an adequate amount of biometric parameter data from the wearer to be obtained by the wearable sensing device to be used as the training data to train a second machine-learned model; receiving, by the computing system as an output of the first machine-learned model, an identification of one or more phases of a menstrual cycle of the wearer; after the first period of time has elapsed, inputting, by the computing system, the biometric parameter data obtained by the wearable sensing device into the second machine-learned model; and receiving, by the computing system as an output of the second machine-learned model, the identification of the one or more phases of the menstrual cycle of the wearer.

[0030] In another aspect, the first machine-learned model can be trained to identify the one or more phases of the menstrual cycle of the wearer using training data obtained from a plurality of individuals that may or may not include the wearer.

[0031] In still another aspect, the second machine-learned model can be trained to identify the one or more phases of the menstrual cycle of the wearer of the wearable sensing device, wherein the second machine-learned model can use training data obtained only from the wearer.

[0032] In yet another aspect, the first machine-learned model is not used after the first period of time has elapsed so that only the second-machine learned model is used to identify the one or more phases of the menstrual cycle of the wearer after the first period of time has elapsed using biometric parameter data that is continuously input into the second machine-learned model.

[0033] In an additional aspect, the wearable sensing device can include a first wearable sensing device and a second wearable sensing device, wherein the first wearable sensing device and the second wearable sensing device are each inserted into a created cavity of the wearer.

[0034] In one more aspect, the method can include measuring the one or more biometric data parameters with the first wearable sensing device and the second wearable sensing device simultaneously.

[0035] In another aspect, the method can include measuring the one or more biometric data parameters with the first wearable sensing device while the second wearable sensing device is not activated and vice versa.

[0036] In yet another aspect, the one or more biometric data parameters can include created cavity temperature, SpO2, active energy expenditure, resting energy expenditure, total energy expenditure, sleep metrics, heart rate, heart rate variability, physical activity, or a combination thereof.

[0037] In still another aspect, the first machine-learned model, the second machine- learned model, or both can include one or more of a deep artificial neural network, a transformer network with attention mechanisms, a support vector machine, a decision tree, or a linear model. The deep artificial neural networks can include architectures that employ attention algorithms, such as self-attention and multi-head attention mechanisms, to enhance pattern recognition and feature extraction from sensor data.

[0038] In one more aspect, the method can include inputting, via the computing system, data corresponding with the one or more biometric data parameters and information related to the identification of the one of more phases of the menstrual cycle of the wearer into a third machine-learned model; and outputting, by the computing system via the third machine learned model, predictions related to the wearer’s physical health, mental health, stress level, readiness, restorative shift, resilience, or a combination thereof. Further, the third machine-learned model can include one or more of a large language model or a generative Al model.

[0039] In a further aspect, the method can include inputting, via the computing system, data corresponding with the one or more biometric data parameters and information related to the identification of the one of more phases of the menstrual cycle of the wearer into a fourth machine-learned model; and outputting, by the computing system via the fourth machine-learned model, recommendations to improve the wearer’s physical health, mental health, stress level, readiness, restorative shift, resilience, or a combination thereof. Further, the fourth machine-learned model can include one or more of a large language model or a generative Al model.

[0040] These and other features, aspects, and advantages of various embodiments of the present disclosure will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate example embodiments of the present disclosure and, together with the description, serve to explain the related principles.

[0041] The above summary does not include an exhaustive list of all aspects of the present disclosure. It is contemplated that the disclosure includes all systems and methods that can be practiced from all suitable combinations of the various aspects summarized above, as well as those disclosed in the Detailed Description below and particularly pointed out in the Claims section. Such combinations may have particular advantages not specifically recited in the above summary. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Several aspects of the disclosure here are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings in which like references indicate similar elements. It should be noted that references to "an" or “one” aspect in this disclosure are not necessarily to the same aspect, and they mean at least one. Also, in the interest of conciseness and reducing the total number of figures, a given figure may be used to illustrate the features of more than one aspect of the disclosure, and not all elements in the figure may be required for a given aspect.

[0043] FIG. 1 illustrates an exploded perspective view of one exemplary embodiment of a wearable sensing device in the form and shape of a post-type earring.

[0044] FIG. 2 illustrates the assembled wearable sensing device of FIG. 1.

[0045] FIG. 3A, FIG. 3B, FIG. 3C, and FIG. 3D illustrate various views of a flexible circuit board and battery of the wearable sensing device of FIG. 1 .

[0046] FIG. 4 illustrates a wearable sensing device system according to one embodiment of the present disclosure.

[0047] FIG. 5 illustrates various devices and components capable of communicating with each other according to one embodiment of the present disclosure.

[0048] FIG. 6 illustrates an example computing system for a machine-learning based outcome predictor determination or a wearer-specific recommendation determination based on the one or more biometric data parameters for the wearer.

[0049] FIG. 7 shows an example of an integrated health monitoring system with body- worn sensing, in accordance with some embodiments.

[0050] FIG. 8 shows a flow diagram of an example method for performing health monitoring with body-worn sensing based on HRV, in accordance with some embodiments.

[0051] FIG. 9 shows a flow diagram of an example method, performed by a body-worn sensing device, in accordance with some embodiments.

[0052] FIG. 10 illustrates an example of a computing device, in accordance with some aspects. Repeat use of reference characters in the present specification and drawings is intended to represent the same or analogous features or elements of the present disclosure. [0053] FIG. 11 illustrates an example of a user interface and device, in accordance with some embodiments.

[0054] FIG. 12 illustrates an example of a user interface and device, in accordance with some embodiments.

DETAILED DESCRIPTION

[0055] Increased consumer interest in personal health has led to a variety of personal health monitoring devices being offered in the market. For example, wearable devices for monitoring personal health are well known in the art, which include electronic measurement devices that can be worn on a finger, wrist, arm, or other body part. Generally, such devices include electronic elements, such as one or more flexible printed circuit boards, processors, sensors, batteries, and the like, with the device being worn close to, on, and/or in contact with a surface of skin, where the devices detect, analyze, and transmit information concerning an individual’s biometric signals such as vital signs, and/or ambient data and which allow in some cases immediate biofeedback to the wearer. Also commonly referred to as wearables, fashion technology, smartwear, tech togs, streetwear tech, skin electronics or fashion electronics, wearable devices such as activity trackers are an example of the Internet of Things, since "things" such as electronics, software, sensors, and connectivity are effectors that enable objects to exchange data through the internet with a manufacturer, operator, and/or other connected devices, without requiring human intervention. Wearable devices are popular in consumer electronics, most commonly in the form factors of smartwatches, smart rings, and implants. Most wearable devices measure a variety of body conditions (i.e. , temperature, heart rate variability, blood oxygenation levels, pulse rate, breathing rate, and so on) on a person’s skin, and often perform other functions. Typically, data collected from a wearable device has not been analyzed in the context of woman’s unique menstrual cycle. This can lead to less accurate historical data and predictions geared towards improving a woman’s health.

[0056] As such, a need currently exists for a wearable device and method of tracking an individual or a woman’s health (e.g., based on her unique menstrual cycle) in order to provide accurate tracking and outcome predictions that are individualized to provide useful feedback, suggestions, and options for a woman to manage her unique health. [0057] Additionally, current approaches to physiological stress monitoring typically treat stress as a series of isolated events, focusing on momentary fluctuations in heart rate variability (HRV) without accounting for the cumulative nature of stress and recovery throughout the day. This "isolated event" perspective fails to capture the ongoing buildup of physiological stress and does not provide users with actionable insights into their long-term stress resilience or recovery needs. As a result, users may not receive timely or personalized guidance to prevent chronic stress accumulation, which can lead to burnout, mental health disorders, and diminished well-being.

[0058] Aspects of the present disclosure address deficiencies in health or stress monitoring, by collecting HRV and related biometric data from a body-worn sensing device, to calculate a personalized daily threshold that distinguishes between an "activated state" that corresponds to stress accumulation, and a "recovery state" that corresponds to parasympathetic dominance. By dynamically assessing the user’s physiological state in real-time and determining and presenting stress and recovery patterns, the disclosed system helps users to seek or maintain a target stress level. The system can output tailored recovery notifications and prevent the long-term negative effects of chronic stress based on targeted benchmarks such as the user's HRV upon waking up, coupled with historical user data or general population data. This cumulative and personalized approach to stress and health offers an improvement to static physiological stress monitoring techniques.

[0059] It is to be understood by one of ordinary skill in the art that the present discussion is a description of exemplary embodiments only, and is not intended as limiting the broader aspects of the present disclosure. Any of the features, components, or details of any of the arrangements or embodiments disclosed in this application are interchangeably combinable with any other features, components, or details of any of the arrangements or embodiments disclosed herein to form new arrangements and embodiments.

[0060] The present disclosure is generally directed towards a wearable sensing device to measure various physiological conditions of a wearer. Such measurements can include sensing a useful body temperature and/or variations thereof over time, heart rate, breathing rate, blood oxygenation, pulse, movement, activity, calories, distance traveled, steps, blood pressure, glucose monitoring, angular velocity measurements, location (e.g. global positioning), magnetic field measurements, and ambient noises and other conditions.

[0061] Among other things, the device is capable of measuring the created cavity temperature (CCT) within an artificial created cavity in living tissue or the wearer’s body into which the device is inserted. This current device provides a wearable thermometer that continuously or periodically measures temperature for a convenient, comfortable method of continuously tracking the wearer’s temperature. Alternatively or in combination, the device may include LED sensors for heart rate, breathing rate, blood oxygenation and/or pulse measurements. Alternatively or in combination, the device may include an accelerometer for movement, activity, calories, distance traveled and/or steps. Alternatively or in combination, the device may include LED sensors and/or Pressure sensors for blood pressure. Alternative or in combination, the device may include a CGM sensor for glucose monitoring. Alternatively or in combination, the device may include a gyroscope for angular velocity measurements. Alternatively or in combination, the device may include a global positioning system sensor. Such features are used to collect data from the wearer that can then be used to make predictions and provide recommendations specific to the wearer based on the wearer’s own biometric data and menstrual cycle information with respect to physical health, mental health, sleep, stress, readiness, restoration, resilience, etc.

[0062] Specifically, in one embodiment of the present disclosure, a computing system is contemplated that includes a first machine-learned model trained to identify one or more phases of a menstrual cycle of a wearer of the wearable sensing device. The first machine-learned model uses training data obtained from a plurality of individuals that may or may not include the wearer. The computing system also includes a second machine-learned model trained to identify the one or more phases of the menstrual cycle of the wearer of the wearable sensing device. In contrast to the first machine-learned model, the second machine-learned model uses training data obtained only from the wearer. The system also includes one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include: obtaining biometric parameter data from the wearer as measured via the wearable sensing device; inputting the biometric parameter data into the first machine-learned model for a first period of time, the first period of time being sufficient for an adequate amount of biometric parameter data from the wearer to be obtained by the wearable sensing device to be used as the training data to train the second machine-learned model; receiving, as an output of the first machine-learned model, the identification of the one or more phases of the menstrual cycle of the wearer; after the first period of time has elapsed, inputting the biometric parameter data obtained by the wearable sensing device into the second machine-learned model; and receiving, as an output of the second machine-learned model, the identification of the one or more phases of the menstrual cycle of the wearer. [0063] Wearable Sensing Device Components are described.

[0064] Referring now to FIG. 1 - FIG. 6, the particular components of the wearable sensing device are shown. Specifically, FIG. 1 depicts a simplified exploded perspective view of a body-mounted wearable sensing device 100 in the form and shape of a posttype earring, while FIG. 2 depicts the assemble view of the wearable sensing device 100. The wearable sensing device 100 includes a central body 110, a base 120, a post 130, a surface dome or cover 140 and a clasp or backing 150. Further, it is to be understood that although FIGs. 1 and 2 only show one wearable sensing device 100, a pair of wearable sensing devices 100 are contemplated by the present disclosure for, inter alia, improved accuracy, additional data points, battery conservation, and the like.

[0065] As best seen in FIG. 3A through FIG. 3D, the central body 300 can include a semi-flex board, flex board or flexible circuit board 310 having a first rigid section 320, a second rigid section 330, and a flexible linkage 340 positioned therebetween and connecting the two rigid sections. The shape of the two rigid sections is desirably round to fit within the dome enclosure. Exemplary dimensions for the flexboard components can include a thickness of 825 pm (±120 pm) and a diameter of approximately 10 mm for each rigid section, with a 133 pm thickness (± 50 pm) for a 6 mm long flexible section having a width of 3.48 mm and a 0.55 mm radius of flex.

[0066] In the depicted embodiment, a power supply or battery 350 can be positioned or sandwiched between the first and second rigid sections for sizing considerations and/or a variety of other reasons, although a wide variety of component configurations and/or arrangements can be utilized by those of ordinary skill in the art of circuit design. In various embodiments, the disclosed devices desirably utilize minimal power and have the capability to measure a wearer’s temperature or other vital signs or biodata via the earring post within the ear piercing (and/or other measurements) at least every 15 mins for a minimum of eight (8) hours before requiring recharging. More preferably, the disclosed devices allow for the capability to measure a wearer’s temperature via the earring post within the ear piercing every 15 seconds for a minimum of twenty-four (24) hours before requiring recharging. As described herein, the carrying and/or storage case for the earrings may incorporate battery charging features which are integrated into the portable storage case.

[0067] Various types of batteries may be utilized for any of the components described herein, including films, flex, rechargeable, non-chargeable, electronic charging, solarpower charging, trickle charging, battery maintainers etc.

[0068] FIGs. 3C and 3D depict the exemplary flexible circuit board 310 of FIGs. 3A and 3B after being flexed to a desired “stacked” configuration, with a battery 350 (e.g., a 3.7 Volt 13 m-A-hr Li-ION battery commercially available from Shenzhen Grepow Battery Co., Ltd. of Shenzhen, China) desirably positioned between the rigid sections 320 and 330 with battery leads 360 shown extending along and/or connected to ports on the second rigid section 330. The first rigid section will desirably house the MCU, memory and RF antenna, with this section preferably thermally isolated to some degree from the more heat generating second rigid section, as well as isolating the digital signals from the analog signals. The second rigid section will desirably house the power, analog sensors, post assembly and battery (which can be soldered as a post process of SMD placements). The battery leads are desirably soldered to castellated edge connections on this section. The first rigid section can also incorporate castellated edge connections for SWD Debug/Programming Interface between MCU and External Debugger/Programmer.

[0069] In various embodiments, a miniature transmitter is included on the flexboard and is used for interfacing the wearable sensor, LED sensors, accelerometer, and other sensors to a measurement tracking or control device. The transmitter can be positioned on an end of the device and is desirably located outside of the artificial created cavity. The transmitter contains the capability to isolate, amplify, filter noise, linearize, and convert input signals from the data sensors and wearable sensor and send a standardized output signal to the computing/control device. Common electrical output signals ranges are used. [0070] As best seen in FIGs. 3B through 3D, a central post 370 will desirably extend through and be secured within an opening in the second rigid section 330. In a preferred embodiment, an overall dimension of the earring device can be an outer diameter of 12 mm to 13 mm, with the dome shaped and/or colored to present a pearl-like appearance or other desired colors or styles. In various alternative embodiments, the device can have a width/diameter of approximately less than 16 mm, and more preferably less than 12 mm. The post can have a length of approximately 6 mm to 8 mm and a diameter of approximately 1 mm, although other posts having lengths of 4.5 mm, 6 mm, 8 mm and 10 mm and diameters/post thicknesses such as 0.8 mm, and 1 .2 mm (and/or other sized known in the art) are contemplated herein with various design changes. The earrings can each have a total weight of less than 8 grams, and more preferably less than 5 grams, and desirably incorporates a comfortable and lightweight design and outer profile to allow the wearer to wear the earrings during sleep.

[0071] As best seen in FIGs. 1 and 2, the base component 120 will desirably fit partially and/or fully within the cover 140 (e.g., preferably via seamless outer and inner molding), with a lower surface of the base positioned adjacent to or against a skin surface of the wearer (e.g., an ear surface for an earring embodiment) when the post is contained within the piercing channel. In various embodiments, the device components will desirably be fully sealed and capable of full water immersion, including during bathing and/or showers. In some embodiments, additional water protections may be provided, such as 1 meter, 3 meter and/or 100 meter waterproof / water resistance measures.

[0072] For one non-limiting example, the cover component can include a small, spherical housing, approximately 0.25 inch diameter, containing all electrical components needed to operate the wearable sensors and/or biosensors and/or temperature sensors and/or fluid sensors and associated components. Such a design can desirably emulate the profile, shape and/or coloration of a small, pearl earring or similar design.

[0073] While the disclosed embodiment is a one-piece earring, it is contemplated that an alternative design could incorporate a two-piece construction, such as where the wearable sensing device 100 includes two parts, including a proximal portion that is able to be disconnected from a distal portion. For example, the distal portion could include a miniature transmitter, other components and/or at least one wearable sensor and biosensor and temperature sensor, while the proximal removable portion could include a miniature battery. The proximal portion can be connected to the distal portion by means of the post (e.g., containing a temperature sensor and/or other biosensors and/or wearable sensors and/or microphone and/or speaker), with the proximal portion encompassing the capability to detach by manually sliding off the post, similar to an earring back. By removing the proximal portion of the device, inserting the distal portion of the device through a created cavity, and returning the proximal portion onto the device to provide energy for the various components thereof, CCT or other biometrics can be measured and tracked.

[0074] In various embodiments, as shown in FIG. 1 , the post 130 and/or base 120 can desirably incorporate open and/or clear/transparent portions which allow various components to access the skin surface and/or transmit/receive information from the wearer’s anatomy, such as LED transmitters and/or sensors to detect and/or calculate wearer anatomical measurements such as heart rate, pulse, breathing rate, blood oxygenation and/or CO2 levels. A variety of sensor types can be incorporated into the device, including a wide variety of biochemical (enzyme-based, tissue-based, immunosensors, DNA biosensors, and thermal and piezoelectric biosensors), chemical, electromechanical, optical and/or electrical sensors. For example, a chemical sensor may be included to measure the concentration levels of chemicals in blood, sweat or other bodily fluids, such as glucose monitors for diabetics and/or lactate level measurements, as well as sensors to measure proteins or hormones, fertility hormones or other chemical constituents (e.g., stress hormones) in sweat. Similarly, an electromechanical sensor can be incorporated to use electrical measurements to track mechanical movements, such as an accelerometer to measure physical activity and/or device/wearer orientation, inertial measurement units to measure angular changes and/or linear acceleration (e.g., for rotational velocity and/or position tracking) or GPS. Optical sensors can be incorporated to detect various biological signals like heart rate, heart rate variability, pulse, breathing rate, oxygen saturation and/or blood pressure (as well as temperature, galvanic skin response and/or stress sensors), with these sensors typically including a light source (transmitter or emitter) and photodiode sensors (detectors or receivers) that measure how much light is absorbed, reflected back out and/or passed through adjacent tissues via spectroscopy analysis. Electrical sensors (including bioelectrical sensors and electrochemical sensors) can be included to detect, measure and evaluate electrical signals in the wearers tissues, including to measure heart rate or brain activity, including electrocardiogram information (e.g., ECG or heart rate monitor), EEG measurements (electroencephalograms) electromyography measurements (EMG or muscle movement monitor) and/or electrode dermal sensors to measure sweat levels (e.g., perspiration monitoring). Other sensors contemplated herein include pressure sensors, continuous glucose monitoring sensors, gyroscopes, GPS receivers and other wearable sensors.

[0075] In various embodiments, combinations of the following measurements and/or sensors are contemplated (including in any combinations thereof): Temperature, Pulse, Resting Heart Rate, Heart Rate Variability (HRV), Heart Beat Sensor, Perfusion, Oxygen Level, Blood Oxygen Level (SpO2), Breathing Rate, Blood Pressure, Glucose, Hormones, GPS, Accelerometer, Motion Sensors, ambient and/or cavity microphones and speakers.

[0076] Where a measurement sensor or wearable sensor, for a non-limiting example wearable sensor is incorporated into the device, this is desirably a small linear or nonlinear rod-like structure, located within and/or on a structure that passes into or through the created artificial cavity, with the sensor in electronic communication with a circuit board containing operational software for the sensor. The wearable sensor can desirably sense or measure temperature or temperature changes constantly or periodically using specific or nonspecific time intervals. In one non-limiting example, this sensor functions to accurately detect small temperature changes, for measurement orders of about 1 degrees to about 0.01 degrees Fahrenheit. The temperature can be a unitary sensor unit, or a plurality of sensors can be used.

[0077] In one exemplary embodiment disclosed herein, shown in FIG. 1 , the wearable sensing device 100 desirably incorporates a plurality of wearable sensors, including at least one temperature sensor (e.g., thermistor sensor SC30F103AN, commercially available from Amphenol Thermometries, Inc. of St. Marys, PA, USA) positioned within the post 130, an ambient temperature sensor (e.g., NTC thermistor NCP03XH103J05RL commercially available from Murata Electronics North America, inc. of Smyrna, GA USA) on the central body 110, an accelerometer (e.g., accelerometer MC3635 commercially available from Memsic Semiconductor Co., Ltd. of Zhubei City, Hsinchu County, Taiwan), and an optical sensor package which incorporates an optical biosensor with proximity sensor and ambient light sensing features (Renesas OB1203SD-C4, commercially available from Renesas Electronics Corporation of Tokyo, Japan). The Renasas photoplethysmography (PPG) biosensor integrates light sources and drivers, analog digital conversion and I2C communication in a single optical package, with data from the OB1203 biosensor potentially being used to determine heart rate (HR), oxygen saturation (SpO2), respiration rate (RR), pulse and/or heart rate variability (HRV - a measure of stress). In various embodiments, this device can desirably measure one or more of the following: heart rate, heart rate variability, oxygen saturation, respiration rate, 3-axis accelerometer, ear lobe cavity temperature and/or ambient temperature.

[0078] Referring now to FIG. 4, components of an example system 400 of the wearable sensing device 100 that can be utilized in accordance with various embodiments are illustrated. In particular, as shown, the system 400 may also include at least one controller 402 communicatively coupled to the plurality of biometric sensors 170 contained within or on the wearable sensing device 100. Moreover, in an embodiment, the controller(s) 402 may be a central processing unit (CPU) or graphics processing unit (GPU) for executing instructions that can be stored in a memory device 404, such as flash memory or DRAM, among other such options.

[0079] For example, in an embodiment, the memory device 404 may include RAM, ROM, FLASH memory, or other non-transitory digital data storage, and may include a control program comprising sequences of instructions which, when loaded from the memory device 404 and executed using the controller(s) 402, cause the controller(s) 402 to perform the functions that are described herein. As would be apparent to one of ordinary skill in the art, the system 400 can include many types of memory, data storage, or computer-readable media, such as data storage for program instructions for execution by the controller or any suitable processor. The same or separate storage can be used for images or data, a removable memory can be available for sharing information with other devices, and any number of communication approaches can be available for sharing with other devices.

[0080] In addition, as shown, the system 400 can include any suitable external display 406, such as a touch screen, organic light emitting diode (OLED), or liquid crystal display (LCD) on a mobile phone, tablet, or computer, although devices might convey information via other means, such as through audio speakers, projectors, and the present disclosure contemplates casting the display or streaming data to another device, such as a mobile phone, tablet, or computer, wherein an application on the mobile phone displays the data obtained from the wearable sensing device 100. The system 400 may also include one or more wireless components 412 operable to communicate with one or more electronic devices within a communication range of the particular wireless channel. The wireless channel can be any appropriate channel used to enable devices to communicate wirelessly, such as Bluetooth, cellular, NFC, Ultra-Wideband (UWB), or Wi-Fi channels. It should be understood that the system 400 can have one or more conventional wired communications connections as known in the art.

[0081] The system 400 also includes one or more power components 408, such as may include a battery operable to be recharged through conventional plug-in approaches, or through other approaches such as capacitive charging through proximity with a power mat or other such device. In further embodiments, the system 400 can also include at least one additional I/O device 410 able to receive conventional input from a wearer. This conventional input can include, for example, a push button, touch pad, touch screen, wheel joystick, keyboard, mouse, keypad, or any other such device or element whereby a wearer can input a command to the system 400. In another embodiment, the I/O device(s) 410 may be connected by a wireless infrared or Bluetooth or other link as well in some embodiments. In some embodiments, the system 400 may also include a microphone or other audio capture element that accepts voice or other audio commands. For example, in particular embodiments, the system 400 may not include any buttons at all, but might be controlled only through a combination of visual and audio commands, such that a wearer can control the wearable sensing device 100 without having to be in contact therewith. In certain embodiments, the I/O elements 410 may also include one or more of the biometric sensors 170 described herein, optical sensors, barometric sensors (e.g., altimeter, etc.), and the like.

[0082] Still referring to FIG. 4, the system 400 may also include a driver 414 and at least some combination of one or more emitters 416 and one or more detectors 418 (referred to herein as an optics package 415) for measuring data for one or more metrics of a human body, such as for a person wearing the wearable sensing device 100. In such embodiments, as shown in FIG. 4, for example, the optics package 415 may be arranged within the central body 110, base 120, post 130, surface dome or cover 140, clasp or backing 150 and at least partially exposed through an exterior surface of the wearable sensing device 100. Thus, as shown and further explained herein, the biometric sensors 170 may be positioned around the optics package 415 on an exterior surface of the wearable sensing device 100. In alternative embodiments, the various components of the optics package 415 may be positioned around the biometric sensors 170 and/or in another other suitable configuration such as adjacent to, interspersed with, surrounded by, or on top of the optics package 415. In certain embodiments, for example, wherein the biometric sensors 170 are transparent, the biometric sensors 170 may be arranged atop the optics package 415.

[0083] The emitters 416 and detectors 418 of FIG. 4 may also be capable of being used, in one example, for obtaining optical polyplethysmography (PPG) measurements. Some PPG technologies rely on detecting light at a single spatial location, adding signals taken from two or more spatial locations, or an algorithmic combination thereof. Both of these approaches result in a single spatial measurement from which the heart rate (HR) estimate (or other physiological metrics) can be determined. In some embodiments, a PPG device employs a single light source coupled to a single detector (i.e. , a single light path). Alternatively, a PPG device may employ multiple light sources coupled to a single detector or multiple detectors (i.e., two or more light paths). In other embodiments, a PPG device employs multiple detectors coupled to a single light source or multiple light sources (i.e., two or more light paths). In some cases, the light source(s) may be configured to emit one or more of green, red, infrared (IR) light, as well as any other suitable wavelengths in the spectrum (such as long IR for metabolic monitoring). For example, a PPG device may employ a single light source and two or more light detectors each configured to detect a specific wavelength or wavelength range. In some cases, each detector is configured to detect a different wavelength or wavelength range from one another. In other cases, two or more detectors are configured to detect the same wavelength or wavelength range. In yet another case, one or more detectors configured to detect a specific wavelength or wavelength range different from one or more other detectors). In embodiments employing multiple light paths, the PPG device may determine an average of the signals resulting from the multiple light paths before determining an HR estimate or other physiological metrics. [0084] Moreover, in an embodiment, the emitters 416 and detectors 418 may be coupled to the controller 402 directly or indirectly using driver circuitry by which the controller 402 may drive the emitters 216 and obtain signals from the detectors 418. The host computer 422 can communicate with the wireless networking components 412 via the one or more networks 420, which may include one or more local area networks, wide area networks, UWB, and/or internetworks using any of terrestrial or satellite links. In some embodiments, the host computer 422 executes control programs and/or application programs that are configured to perform some of the functions described herein.

[0085] Referring now to FIG. 5, a schematic diagram of an environment 500 in which aspects of various embodiments can be implemented is illustrated. In particular, as shown, a wearer might have a number of different devices that are able to communicate using at least one wireless communication protocol. For example, as shown, the wearer might have a wearable sensing device 100, which the wearer would like to be able to communicate with a smartphone 504 and a tablet computer 506. The ability to communicate with multiple devices can enable a wearer to obtain information from the wearable sensing device 100, e.g., data captured using a sensor on the wearable sensing device 100, using an application installed on either the smartphone 504 or another such device 506 associated with that wearer, such as but not limited to a tablet, personal computer, smartwatch, and the like. The system may also automatically and periodically sync data from the wearable sensing device 100 to a service provider 508, or other such entity, which is able to obtain and process data from the wearable sensing device 100 and provide functionality that may not otherwise be available on the wearable sensing device 100 or the applications installed on the individual devices. In this embodiment, the wearable sensing device 100 automatically communicates via Bluetooth® with one of the individual devices (such as smartphone 504 or tablet computer 506) to periodically sync data in the background without requiring user intervention, and the individual device then communicates with the service provider 508 through at least one network 220, such as the Internet or a cellular network. There may be a number of other types of, or reasons for, communications in various embodiments. [0086] In addition to being able to communicate, the system may also need to communicate in a number of ways or with certain aspects. For example, communications between the devices should be secure, particularly where the data may include personal health data or other such communications. The device or application providers may also be required to secure this information in at least some situations. The devices should be able to communicate with each other concurrently, rather than sequentially, to enable seamless background data synchronization. This may be particularly true where pairing may be required, as it is preferable that each device be paired at most once, such that no manual pairing is required for subsequent automatic sync operations. The communications should also be as standards-based as possible, not only so that little manual intervention is required but also so that the devices can automatically communicate with as many other types of devices as possible, which is often not the case for various proprietary formats. The system is thus designed to enable automatic communication between devices with little to no effort required from the wearer, allowing for seamless background data synchronization when devices are within communication range. In various conventional approaches, a device will utilize a communication technology such as Wi-Fi to communicate with other devices using wireless local area networking (WLAN). Smaller or lower capacity devices, such as many Internet of Things (loT) devices, instead utilize a communication technology such as Bluetooth®, and in particular Bluetooth Low Energy (BLE) which has very low power consumption.

[0087] In further embodiments, the environment 500 illustrated in FIG. 5 enables data to be captured, processed, and displayed in a number of different ways. For example, data may be captured using sensors on the wearable sensing device 100, but due to limited resources on the wearable sensing device 100, the data may be transferred to the smartphone 504 or the service provider 508 (or a cloud resource) for processing, and results of that processing may then be presented back to the wearer of the wearable sensing device 200 via a smartphone 504, and/or another such device 506 associated with that wearer, such as but not limited to a table, personal computer, smartwatch, and the like. In at least some embodiments, a wearer may also be able to provide input such as health data using an interface on any of these devices, which can then be considered when making that determination.

[0088] Relating to created body cavity, it should be understood that the disclosed device(s) can be utilized in a variety of locations on/in a human body, although for many individuals a body piercing location such as one or both ear lobes may be particular preferred (and such locations may already be pierced to accept a variety of ornamentation). Various piercing locations may be suitable for biometric measurement and sensing of temperature, heart rate at rest, heart rate during activity, heart rate variability, heart beat sensing, oxygen sensing, blood oxygen level, blood pressure, resting pulse rate, active pulse rate, perfusion, breathing rate, movement, sleep stages (light, non-REM, REM, deep, awake), active energy expenditure, resting energy expenditure, total energy expenditure, SpO2, and/or glucose, measurements of bacteria, white blood cell count, proteins, lipids, salts, fats (or other fluid characteristics, including lymph or other fluid characteristics) as well as a variety of other data including location (via GPS), sound sensors, accelerometer data, etc. In some cases, localized body conditions proximate to a specific piercing location may be particularly well suited for measurement of various physiological conditions, such as a tongue piercing for tracking glucose measurement and/or blood sugar levels.

[0089] The disclosed OCT is a measurement that may be obtained from an artificially created cavity in the wearer’s body, where the created cavity is formed artificially, as in a non-limiting example, an earring piercing procedure. Additionally, two or more artificially created cavities can be utilized for measuring CCT in one body. As one nonlimiting example, two different biosensing devices or wearable sensing devices can be used in two different created cavities to measure two CCT values (or other anatomical metrics) simultaneously within separate created cavities. Further, these different sensing devices can be used to derive a single CCT (or other anatomical metrics). It should be understood that the CCT (or other anatomical metrics) from one created cavity in a wearer’s body may by different from the CCT (or other anatomical metrics) obtained from another created cavity in the same wearer’s body, as anatomical differences may induce localize temperature variations (e.g., the CCT or other metric of an ear piercing may not be the same as the CCT or other metric of a belly button or tongue piercing of the same wearer, or the CCTs or other metrics in opposing ear piercings may be different for a variety of environment and/or anatomical reasons).

[0090] The disclosed device can desirably measure CCT or other metrics within the created cavity to track the wearer’s useful temperature or for other purposes. The aspects of the created cavity are further described below. CCT may or may not be the same temperature measurement as core body temperature (CBT), internal body temperature (IBT), basal body temperature (BBT) body cavity temperature (BCT), and/or surface body temperature (SBT). This can be recognized by reason that the temperature inside the created cavity does not need to equal and/or correlate on a relationship basis with CBT, IBT, BBT, BCT and/or SBT. Moreover, it is suitable if CCT measurements are a perfect match, higher, lower, or not a 1 :1 relationship to body temperature.

[0091] The disclosed device can offer a practical design that provides means for a comfortable, convenient and unobtrusive method of continuously or periodically tracking the wearer’s temperature or other biometrics while sleeping, rest or activity. For example, but not limited to, the shape of the device resembles a small, stud earring structure that is wearable on the body, through a wearer’s earlobe, wearer’s belly button, tongue, nose, eyebrow, lip, genitalia and/or other body locations. This device allows for wireless communication to an external system for tracking continuous or periodic measurements, which can be used for the non-limiting example of determination of fertility by identifying changes in body temperature associated with the biological event of ovulation.

[0092] By means of the disclosed device, cavity temperature measurements and/or other measurements are obtainable from a wearer over a defined period of time. In a non-limiting example, cavity temperature measurements can help assist in the determination of fertility. Within this example, readings are evaluated to differentiate the local minimum during a given night’s sleep, where the local minimum is defined by the lowest reading within a pre-established time period. Then local minima can be plotted in relation to time for fertility trends. Additionally, measurements can be tracked over 24 hours and temperatures compared across different days and nights to determine patterns, for a non-limiting example, to identify or predict the wear’s date of ovulation.

[0093] Related to Cycle and Ovulation/Fertility Detection, 12.3 percent of women (7.5 million) in the United States ages 15-44 have impaired ability getting or staying pregnant, according to the Centers for Disease Control and Prevention (CDC). In 40% of such women, ovulatory defects are present but difficult to characterize because ovulation is an internal, normally unmonitored clinical state that evolves quickly over short time frames. Defining the rapidly changing physiology of ovulation for each affected woman requires frequent monitoring during suspected ovulation windows. Moreover, while the rhythm method of birth control (e.g., tracking a menstrual cycle on a calendar to predict ovulation) is over 75% effective at preventing unwanted pregnancy, less than 1 % of women 15 to 44 years of age currently use this highly effective natural form of birth control.

[0094] In predicting ovulation for both pregnancy and birth control objectives, the need for frequent monitoring currently translates to significant patient burdens. The use of basal body temperature (BBT) recordings is a known and safe method to monitor ovulation and has the advantages of patient monitoring themselves at home (no clinical scheduling or attendance) at minimal cost and risk compared to a blood tests or ultrasounds. However, application of BBT methods is limited by the inconvenience of taking and recording a waking temperature at the same time each morning - patient compliance is especially poor. Even for those who comply, the information is difficult to interpret and often frustrating for the patient.

[0095] Traditional fertility thermometers assist in tracking a woman’s ovulation trends by measuring her basal body temperature (BBT) through a natural body cavity. These devices do not offer the most accurate ovulation results due to temperature measurements taken after awakening with a non-convenient thermometer. In contrast, the disclosed devices and associated system components will desirably eliminate any need to awaken before temperature measurements can be taken and more accurately identify the low temperature within a given night’s sleep, because the low temperature does not necessarily occur at the time of waking. This is desirably accomplished by a small, wearable sensor such as a temperature sensor located within an artificial created cavity in the body that offers continuous or periodic readings of CCT that are wirelessly transmitted and analyzed by the wearer’s associated smart device.

[0096] In one exemplary embodiment, the disclosed earring technology and systems can track a wearer’s unique menstrual cycle (menstruation phase, follicular phase, ovulation phase, luteal phase) and provide insights to the specific wearer based on their current phase in terms of needs related to rest, sleep, nutrition, exercise, and the like. The system conveniently monitors ovulation by measuring basal body temperature (BBT) utilizing earring sensors - similar in visual appearance to the earrings that many women in the US wear daily. Since the monitoring occurs at home, the inconvenience and cost to the patient is mitigated and care can be delivered more equitably. Moreover, the applications of this disclosed device can go far beyond infertility and can extend to use in identifying ovulation / avoiding pregnancy in fertility management, assist with wellness tracking, perform infection / COVID monitoring and/or other uses, provide early pregnancy detection, assist with natural birth control, identify pregnancy, labor and menopause onset, and/or track a wearer’s menstrual cycle and/or other health metrics. In various embodiments, the earring may include features which may alert the wearer (i.e., using sound, vibration, light and/or electrical pulses to the wearer) of vital information, such as device proximity or non-proximity (i.e., phone theft prevention) or the receipt of emails, text messages, information updates or phone calls, etc. The disclosed devices and related system components also provide capabilities to obtain information based on early detection of one’s health issues or conditions, like infection, C0VID19, virus, pregnancy, early labor, perimenopause, women specific conditions or needing to take specific medications or vitamins. This device could also be used for other medical applications, by way of non-limiting example as seen in detection of fever and/or sickness. Further, this device can be used for deriving information for medical tracking and implemented for hospital use in circumstances that require constant or periodic temperature and vital and biometric monitoring, and it should be understood that the device can be used in the context of hospital systems or other medical environments for in-patient and/or remote patient monitoring, where the device can be used to relay information and can be linked to medical provider records. Additional applications contemplated herein include, but are not limited to, fertility, infertility, natural family planning, COVID infection and/or condition, infectious disease symptoms and/or susceptibility, cancer diagnoses and treatment, long term hospital patient monitoring, fever detection and treatment, thyroid issues, gut health, inflammation, hormone health, hormone levels, energy levels, cardiovascular health, post-partum conditions, puberty, PCOS, diabetes detection and management, obesity, asthma, heart disease, chronic obstructive pulmonary disease, women's health issues, wellness management, stress management, sleep monitoring and disorder detection and treatment, diet and calorie counting, activity/fitness tracking, sports, government health, military, agriculture, etc.

[0097] In one embodiment of the present disclosure, a computing system is contemplated that includes a first machine-learned model trained to identify one or more phases of a menstrual cycle of a wearer of the wearable sensing device. The first machine-learned model uses training data obtained from a plurality of individuals that may or may not include the wearer. The computing system also includes a second machine-learned model trained to identify the one or more phases of the menstrual cycle of the wearer of the wearable sensing device. In contrast to the first machine-learned model, the second machine-learned model uses training data obtained only from the wearer. The system also includes one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include: obtaining biometric parameter data from the wearer as measured via the wearable sensing device; inputting the biometric parameter data into the first machine-learned model for a first period of time, the first period of time being sufficient for an adequate amount of biometric parameter data from the wearer to be obtained by the wearable sensing device to be used as the training data to train the second machine-learned model; receiving, as an output of the first machine-learned model, the identification of the one or more phases of the menstrual cycle of the wearer; after the first period of time has elapsed, inputting the biometric parameter data obtained by the wearable sensing device into the second machine-learned model; and receiving, as an output of the second machine-learned model, the identification of the one or more phases of the menstrual cycle of the wearer. [0098] In addition, it is to be understood that the first machine-learned model is not used after the first period of time has elapsed so that only the second-machine learned model is used to identify the one or more phases of the menstrual cycle of the wearer after the first period of time has elapsed using biometric parameter data that is continuously input into the second machine-learned model.

[0099] Further, the one or more biometric data parameters can include created cavity temperature, SpO2, active energy expenditure, resting energy expenditure, total energy expenditure, sleep metrics, energy levels, hormone levels, heart rate, heart rate variability, physical activity, or a combination thereof. Further, data corresponding with the one or more biometric data parameters and information related to the identification of the one of more phases of the menstrual cycle of the wearer can be input into a machine-learned model that outputs predictions related to the wearer’s physical health, mental health, stress level, readiness, restorative shift, resilience, or a combination thereof, as discussed in the appendices that follow, which are incorporated herein by reference. [0100] Additionally, it should be understood that the machine-learned models contemplated by the present disclosure can provide insights or recommendations to a user based on the user's individual biometric data. These insights and/or recommendations may all be calculated or provided from the foundation on a current phase of the user’s menstrual cycle (i. e. , when the user naturally cycling in reproductive years) or not related to the user’s menstrual cycle phases (i.e. , the user is not naturally cycling, the user is on hormonal birth control, the user is post-menopausal, etc). For example, sleep recommendations may or may not be based on the user's current menstrual cycle phase.

[0101] Referring now to FIG. 6, an example computing system 600 for a machinelearning based outcome predictor determination or a wearer-specific recommendation determination based on one or more biometric data parameters for the wearer is provided. FIG. 6 depicts an example computing system 600 for machine-learning-based identification of the phases of a wearer’s menstrual cycle, predictions of health features (physical, mental, stress, sleep, readiness, restoration, resilience, etc.), and recommendations to provide to the wearer based on the biometric parameter data, among other outputs, according to example embodiments of the present disclosure. The example system 600 includes a computing device 602 and a machine learning computing system 630 that are communicatively coupled over a network 680.

[0102] The computing device 602 includes one or more processors 612 and a memory 614. The one or more processors 612 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory 614 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and combinations thereof.

[0103] The memory 614 can store information that can be accessed by the one or more processors 612. For instance, the memory 614 (e.g., one or more non-transitory computer-readable storage mediums, memory devices) can store data 616 that can be obtained, received, accessed, written, manipulated, created, and/or stored. In some implementations, the computing device 602 can obtain data from one or more memory device(s) that are remote from the device 602. [0104] The memory 614 can also store computer-readable instructions 618 that can be executed by the one or more processors 612. The instructions 618 can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructions 618 can be executed in logically and/or virtually separate threads on processor(s) 612.

[0105] For example, the memory 614 can store instructions 618 that when executed by the one or more processors 612 cause the one or more processors 612 to perform any of the instructions, operations, and/or functions described herein.

[0106] According to an aspect of the present disclosure, the computing device 602 can store or include one or more machine-learned models 610. For example, the models 610 can be or can otherwise include various machine-learned models such as a random forest classifier; a logistic regression classifier; a support vector machine; one or more decision trees; a neural network; and/or other types of models including both linear models and non-linear models. Example neural networks include feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, or other forms of neural networks. The models 610 can also include generative Al and/or large language models and can be, in some instances, trained with one or more prompts.

[0107] According to an aspect of the present disclosure, the computing device 602 can store or include one or more machine-learned models 610. For example, the models 610 can be or can otherwise include various machine-learned models such as a random forest classifier; a logistic regression classifier; a support vector machine; one or more decision trees; a neural network; and/or other types of models including both linear models and non-linear models. Example neural networks include feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, deep neural networks, transformer networks utilizing attention mechanisms, or other forms of neural networks. The models 610 can also include deep learning architectures that employ attention algorithms, such as selfattention and multi-head attention mechanisms, which can be particularly useful for processing sequential sensor data and identifying relevant patterns across different time periods. The models 610 can also include generative Al and/or large language models and can be, in some instances, trained with one or more prompts. [0108] The machine learning computing system 630 includes one or more processors 632 and a memory 634. The one or more processors 632 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory 634 can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and combinations thereof.

[0109] The memory 634 can store information that can be accessed by the one or more processors 632. For instance, the memory 634 (e.g., one or more non-transitory computer-readable storage mediums, memory devices) can store data 636 that can be obtained, received, accessed, written, manipulated, created, and/or stored. In some implementations, the machine learning computing system 630 can obtain data from one or more memory device(s) that are remote from the system 630.

[0110] The memory 634 can also store computer-readable instructions 638 that can be executed by the one or more processors 632. The instructions 638 can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructions 638 can be executed in logically and/or virtually separate threads on processor(s) 632.

[0111] For example, the memory 634 can store instructions 638 that when executed by the one or more processors 632 cause the one or more processors 632 to perform any of the operations and/or functions described herein.

[0112] In some implementations, the machine learning computing system 630 includes one or more server computing devices. If the machine learning computing system 630 includes multiple server computing devices, such server computing devices can operate according to various computing architectures, including, for example, sequential computing architectures, parallel computing architectures, or some combination thereof.

[0113] In addition or alternatively to the model(s) 610 at the computing device 602, the machine learning computing system 630 can include one or more machine-learned models 640. For example, the models 640 can be or can otherwise include various machine-learned models such as a random forest classifier; a logistic regression classifier; a support vector machine; one or more decision trees; a neural network; and/or other types of models including both linear models and non-linear models. Example neural networks include feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, deep neural networks, transformer networks utilizing attention mechanisms, or other forms of neural networks. The models 640 can also include deep learning architectures that employ attention algorithms, such as self-attention and multi-head attention mechanisms, which can enable the machine learning computing system 630 to process complex sensor data patterns and perform advanced analytics that may not be feasible on the local computing device 602. The models 640 can also include generative Al and/or large language models and can be, in some instances, trained with one or more prompts. [0114] As an example, the machine learning computing system 630 can communicate with the computing device 602 according to a client-server relationship. For example, the machine learning computing system 640 can implement the machine-learned models 640 to provide a web service to the computing device 602.

[0115] Thus, machine-learned models 610 can located and used at the computing device 602 and/or machine-learned models 640 can be located and used at the machine learning computing system 630.

[0116] In some implementations, the machine learning computing system 630 and/or the computing device 602 can train the machine-learned models 610 and/or 640 through use of a model trainer 660. The model trainer 660 can train the machine-learned models 610 and/or 640 using one or more training or learning algorithms. One example training technique is backwards propagation of errors (“backpropagation”).

[0117] In some implementations, the model trainer 660 can perform supervised training techniques using a set of labeled training data 662. In other implementations, the model trainer 660 can perform unsupervised training techniques using a set of unlabeled training data. The model trainer 660 can perform a number of generalization techniques to improve the generalization capability of the models being trained. Generalization techniques include weight decays, dropouts, or other techniques. The model trainer 660 can be implemented in hardware, software, firmware, or combinations thereof.

[0118] The computing device 602 can also include a network interface 624 used to communicate with one or more systems or devices, including systems or devices that are remotely located from the computing device 602. The network interface 624 can include any circuits, components, software, etc. for communicating with one or more networks (e.g., 680). In some implementations, the network interface 624 can include, for example, one or more of a communications controller, receiver, transceiver, transmitter, port, conductors, software and/or hardware for communicating data. Similarly, the machine learning computing system 630 can include a network interface 664.

[0119] The computing device 602 can also include one or more sensors 604. For example, the one or more sensors 604 can include any type of sensor useful to collect test data from a wearer, including, for example, various forms of biometric data as described above.

[0120] The computing device 602 can also include a wearer input component 620. For example, the wearer input component 620 can include a microphone, a keypad, a keyboard, a click-wheel, buttons, and/or a touch-sensitive screen.

[0121] The computing device 602 can also include an output component 622. For example, the output component 622 can include a speaker, a haptic output component, and/or a display (e.g., a touch-sensitive display).

[0122] As another example, the computing device 602 can transmit information to one or more additional devices 670 (e.g., a smartphone, smartwatch, tablet, personal computer, etc.). The computing device 602 can communicate with the additional computing device(s) 670 over the network 680 and/or via a local, short-range wireless communication protocol (e.g., Bluetooth).

[0123] The network(s) 680 can be any type of network or combination of networks that allows for communication between devices. In some embodiments, the network(s) can include one or more of a local area network, wide area network, the Internet, secure network, cellular network, mesh network, peer-to-peer communication link and/or some combination thereof and can include any number of wired or wireless links. Communication over the network(s) 680 can be accomplished, for instance, via a network interface using any type of protocol, protection scheme, encoding, format, packaging, etc.

[0124] FIG. 6 illustrates one example computing system 600 that can be used to implement the present disclosure. Other computing systems can be used as well. For example, in some implementations, the computing device 602 can include the model trainer 660 and the training dataset 662. In such implementations, the machine-learned models 610 can be both trained and used locally at the computing device 602. As another example, in some implementations, the computing device 602 is not connected to other computing systems.

[0125] FIG. 7 shows an example of an integrated health monitoring system with body- worn sensing, in accordance with some embodiments. The health monitoring system 700 may comprise a body-worn sensing device 704 that is communicatively coupled to a computing device 702.

[0126] In some embodiments, the body-worn sensing device 704 may include one or more device, earrings, etc., as described in FIG. 1 - FIG. 6. In some embodiments, the body-worn sensing device 704 is configured to continuously or periodically collect biometric data from a user 706 throughout the day. The body-worn sensing device 704 is equipped with one or more sensors 710, including at least a photoplethysmography (PPG) sensor 712. In some embodiments, the one or more sensors 710 may comprise one or more of a created cavity temperature (CCT) sensor, an ambient temperature sensor, an accelerometer, or a gyroscope. In some embodiments, the one or more sensors 710 may comprise all of such sensors. These one or more sensors 710 are embedded within the body-worn sensing device 704, allowing for discreet, comfortable, and continuous physiological monitoring.

[0127] The body-worn sensing device 704 may comprise one or more antennas and associated controller, configured to communicate wirelessly with a computing device 702. The computing device 702 may comprise one or more processors, memory, and other components of a computing device. Computing device 702 may be a smartphone, tablet, or cloud-based server, or desktop computer, a laptop computer, a vehicle infotainment system, or other processing device. The body-worn sensing device 704 and computing device 702 may communicate over a communication link 732. The communication link 732 may comprise a wireless communication link such as, for example, Bluetooth, Wi-Fi, or other wireless communication protocols. Data transmission from the body-worn sensing device 704 to the computing device 702 may occur at regular intervals, such as, for example, at least every minute, or at least every 5 minutes, or at least every 10 minutes, or at least every 15 minutes. In some embodiments, the computing device 702 and/or body-worn sensing device 704 may increase or decrease the frequency of the data transmission, depending on the monitoring protocol and user activity, and/or time of day. For example, the data transmission rate may be increased within a first period of time, which may correspond to the early evening, prior to sleep. The data transmission rate may be reduced within a second period of time, such during typical sleep hours, and be increased again during a third period of time, such as when the user 706 wakes up.

[0128] The user 706 is the individual whose physiological state is being monitored. By wearing the body-worn sensing device 704, the user 706 enables the system to sense a continuous stream of biometric data (e.g., first biometric data 708, subsequent biometric data 722). The biometric data (e.g., signals from the one or more sensors 710) form the basis for personalized stress and recovery processing by the computing device 702. The health monitoring system 700 is configured to be user-centric and present actionable insights and feedback that are tailored to the user’s unique physiological patterns, daily routines, sleep patterns, and other contextual factors such as menstrual cycle phase.

[0129] The one or more sensors 710 can capture a range of physiological signals. The PPG sensor 712 detects blood volume changes in the microvascular bed of tissue, enabling the calculation of RR intervals, which are the time intervals between successive heartbeats. These RR intervals are the basis for heart rate variability (HRV) analysis, which is described in other sections. The device may also collect temperature data (both CCT and ambient), motion data from the accelerometer, and orientation data from the gyroscope, providing additional context for interpreting the user’s physiological state, activity type (e.g., walking, running, etc.,), activity intensity.

[0130] Upon waking or at the start of the monitoring period, the body-worn sensing device 704 collects first biometric data 708. This initial data set is transmitted to the computing device 702, where it is processed at processing block 728.

[0131] At block 728, the computing device 702 scans one or more PPG signals in the first biometric data 708 to determine heart rate variability (HRV) of the user 706. In some embodiments, the computing device 702 scans the PPG signal and extracts RR intervals, by finding localized peaks that represent heart activity of the user 706. At block 728, the computing device 702 may apply signal filtering to remove noise and/or other unwanted artifacts. The computing device 702 may determine the HRV (e.g., first HRV 714, second HRV 734) by detecting variations in the RR intervals over a defined time window (e.g., a one-minute time window, a 5-minute time window, a 10-minute time window, etc.).

[0132] HRV quantifies the natural variation in the time interval between consecutive heartbeats, reflecting how much the timing between each beat fluctuates rather than remaining perfectly regular. This variability indicates the health of a user's Autonomic Nervous System (ANS), which governs involuntary bodily functions such as heart rate, breathing, and digestion. The ANS is composed of two main branches: the sympathetic nervous system (SNS), which activates the body’s “fight-or-flight” response and tends to decrease HRV, and the parasympathetic nervous system (PNS), which promotes “restand-digest” functions. A higher HRV is generally associated with a healthier, more resilient, and adaptable nervous system, indicating effective recovery from stress and a predominance of parasympathetic activity. Conversely, a lower HRV typically signals stress, fatigue, illness, or overexertion, suggesting the body is under strain and more influenced by sympathetic activity. HRV may be influenced by a range of factors, including stress levels, sleep quality, exercise habits, nutrition, hydration, alcohol, caffeine, illness, age, and/or genetics. Good sleep, balanced exercise, and effective stress management typically support higher HRV, while poor sleep habits, illness, and chronic stress can reduce it.

[0133] The computing device 702 quantifies these detected variations in heartbeat according to one or more schemes such as, for example, Root Mean Square of Successive Differences (RMSSD) or Standard Deviation of normal-to-normal intervals (SDNN), to determine the HRV. RMSSD is an HRV metric that quantifies the short-term, beat-to-beat variability in heart rate by calculating the square root of the average of the squared differences between successive R-R intervals. This measure of HRV is sensitive to rapid changes in heart rate that are primarily mediated by the parasympathetic nervous system (PNS), making it effective for assessing vagal tone and daily recovery. SDNN measures the overall variability in heart rate by calculating the Standard Deviation of all normal R-R intervals over a given period, reflecting the combined influence of both the sympathetic and parasympathetic branches of the autonomic nervous system. SDNN is more meaningful when derived from longer recording periods and is less reliable in short-term measurements due to its sensitivity to artifacts and breathing patterns. The computing device 702 may determine HRV as RMSSD, SDNN, or a combination of the two. Taken together, they may provide complementary insights into autonomic function, with RMSSD focusing on short-term parasympathetic activity and SDNN representing overall autonomic variability.

[0134] In some embodiments, the accuracy of HRV calculation is enhanced - the computing device 702 may leverage additional sensor data to identify and exclude periods of movement or activity (e.g., above a threshold), poor signal quality, or otherwise unreliable periods of signal capture, so that the resulting HRV better reflects the biological state of user 706. In some examples, the computing device 702 uses a machine learning model which correlates relationships between other factors or signals and the HRV, and adjust the HRV accordingly. For example, the machine learning may be configured to take, as input, the HRV (the first HRV or the second HRV) as well as the activity of the user, the time of day, the temperature of the user or ambient temperature, and output and adjusted HRV accordingly.

[0135] The first HRV 714 serves as the baseline for the day. This first HRV 714 may be calculated during a morning period such as, for example, within a set period of time which may be determined based on sunrise, the user's historic sleep pattern, upon a detected wake up of the user 706, or other period of time that is defined by the computing device 702.

[0136] At block 730, the computing device 702 sets the daily threshold 716 based on the first HRV 714. The daily threshold 716 distinguishes between a user's first state 718 and second state 720. The first state 718 may represent an activated or stress accumulation state, characterized by increased stress to the user 706. The second state 720 may represent recovery or parasympathetic dominance.

[0137] In some embodiments, the computing device 702 sets the daily threshold 716 as the first HRV 714 (e.g., wake-up HRV). In some embodiments, at block 730, the computing device 702 sets the daily threshold 716 as the first HRV 714, and then adjusts this threshold up or down based on one or more contextual factors. These factors may include sleep quality (e.g., as determined by overnight HRV trends and accelerometer data), historical HRV patterns, population averages, ambient and body temperature, detected activity levels, and/or menstrual cycle phase.

[0138] For example, if the user’s sleep quality was poor (e.g., the detected period of sleep of the user 706 was below a threshold duration), the daily threshold 716 may be reduced. Similarly, if the user's sleep quality was above threshold duration, the computing device 702 may raise the daily threshold 716. In another example, if a user's historical HRV trend is declining, the computing device 702 may adjust the daily threshold 716 downward. Conversely, if the user’s HRV is above their personal or population average, the threshold may be set higher. Lowering the daily threshold 716 reflects the user's increased physiological vulnerability to stress, while reducing the daily threshold 716 reflects the user's greater resilience to stress. A user's sleep pattern, historical data such as HRV, and other user specific data, may be stored in data storage 736. Data storage 736 may be integral to computing device 702 and/or stored on a remote device (e.g., a data server) and accessible over a computer network.

[0139] Throughout the day, the body-worn sensing device 704 continues to collect subsequent biometric data 722 at regular intervals, transmitting this data to the computing device 702 over the wireless communication link 732. The computing device processes each new data set, calculating the second HRV 734 using the same or similar HRV analysis described above. The second HRV 734 is then compared to the daily threshold 716 to determine the user’s current physiological state. If the HRV is below the threshold, the computing device 702 deems the user 706 to be in the first state 718, characterized by stress accumulation or increased sympathetic activity. If the HRV is at or above the threshold, the computing device 702 deems the user to be in the second state 720, characterized by increased parasympathetic activity and active recovery from stress.

[0140] The computing device 702 generates an indication 726 of whether the user 706 is in the first state 718 or in the second state 720. The computing device 702 presents this indication 726 to the user 706 via HMI 724. The HMI 724 may be a graphical display, loudspeakers, or other HMI 724 output device. The indication 726 can be a visual indicator (e.g., a text notification, a graphical element or alert) on the graphical display. Additionally, or alternatively, the indication 726 may comprise an audio notification such as a beep or a voice notification. In some examples, the computing device 702 may present this indication 726 in response to when the user 706 transitions from the first state 718 to the second state 720 and/or when the user 706 transitions from the second state 720 to the first state 718. [0141] In some embodiments, the HMI 724 provides real-time feedback, visualizing transitions between the first state 718 and second state 720, such as, for example, an HRV vs. Time graph, with a visual indication of the daily threshold 716 or transitions between states. In some embodiments, the computing device 702 may deliver notifications or actionable recommendations (indication 726). For example, the system may prompt the user to engage in relaxation techniques if the user 706 remains in the first state 718 for beyond a threshold duration. Additionally, or alternatively, if the current time is within a threshold time from a target sleep time, and the user 706 enters the first state 718, the computing device 702 may output the indication 726. In some embodiments, the computing device 702 will not output the indication 726 before that threshold time, allowing for the user to engage unhindered in stress inducing activities, until the threshold time from the target sleep time. In some embodiments, computing device 702 detects a duration of time for a user’s HRV to transition from the first state (activated) to the second state (recovery). For example, upon detecting that the user is in the first state, the computing device may output a notification to the user that the user is in the first state. From that point, the computing device 702 may monitor the duration of time and/or activity of the user to transition to the second state, if at all. Computing device 702 may store this duration of time and use this as a basis to generate an output to the user through the HMI 724, For example, the computing device 702 may generate a graph or other visual component indicating this time duration, or average or trend thereof, and display this through HMI 724. In some embodiments, the computing device 702 tracks and/or displays a pattern of these transitions to the user, which may be determined over the current day, or over multiple days. Examples of an HMI 724 are shown with respect to FIG. 11 and FIG. 12.

[0142] FIG. 11 shows an example of a user interface and device, in accordance with some embodiments. An indication 1210 may be displayed to a user indicating an HRV threshold, for example, indicating an increased vulnerability to stress. An indication 1212 may comprise guidance to the user to perform stress reduction activities, and may receive input from the user to set one or more reminders to adjust the user’s activities. In addition, an indication 1210 may indicate a graphical representation that shows a timeline of when the user was in the first state and/or in the second state. [0143] FIG. 12 shows another example of a user interface and device, which the system may generate in addition to, or an alternative to FIG. 11. In addition, it should be noted that the various indications (e.g., 1202, 1212, 1210, 1204, 1206, 1208) may be displayed alone, or in different combinations. Indication 1204 may display an accumulation of stress of the user (e.g., in the current day, the previous day, or a custom specified time period), which may be calculated based on a user’s duration in the first state. Additionally, or alternatively, the user interface may display an indication 1206 indicating sleep quality. Additionally, or alternatively, the user interface may display an indication 1208 indicating where the user may be in a menstrual cycle. Other factors used to adjust the daily threshold or user HRV determination can be visually displayed on the HMI. Indications 1214 and 1216 are further examples of visual components that the system can generate and output to HMI 724. Indication 1214 may include a graph 1220 which indicates a target downtime. When the system detects the current time to be near or past this target time, and the user’s state is in the first state, the system can generate one or more notifications to the user. In addition, the indication 1214 may include a visual indicator 1218 of the user’s current state. Indication 1216 may further include a graph indicating the user’s state being in an active state or a recovery state, as well as a visual indication 1222 that includes a transition time of the user from one state to another, such as from the first state to the second state.

[0144] Referring back to FIG. 7, in some embodiments, the body-worn sensing device 704 and computing device 702 work together each day to generate the daily threshold 716. Each morning, the computing device 702 may generate a new daily threshold 716 based on the first biometric data 708 of that day, and based on the other factors described. As such, the daily threshold 716 of a first day can be different from the daily threshold 716 of a second day. In some embodiments, the body-worn sensing device 704 and the computing device 702 are the same device. For example, the operations and components described with respect to computing device 702 may be integrated within the body-worn sensing device 704.

[0145] In such a manner, the health monitoring system 700 integrates advanced multisensor data acquisition, robust HRV analytics, and a dynamic threshold setting, and user-centric feedback to provide a comprehensive solution for tracking and managing physiological stress and recovery. The use of a body-worn sensing device 704 enables discreet, continuous monitoring. The computing device and body-worn sensing device 704 work together to generate a user's HRV, set and adjust personalized daily thresholds, and deliver actionable insights to the user 706.

[0146] FIG. 8 shows a flow diagram of an example method for performing health monitoring with body-worn sensing based on HRV, in accordance with some embodiments. The method 300 can be performed by a computing device or processing logic thereof. Processing logic may comprise hardware (circuitry, dedicated logic, etc.), software (machine executable instructions stored in computer readable memory), firmware, or a combination. A computing device may comprise a single computing device or a combination of computing devices that can be communicatively coupled to each other over a computer network.

[0147] Method 800 may correspond to operations performed by a computing device described in other sections, such as, for example, FIG. 7.

[0148] At block 802, processing logic receives first biometric data from a body-worn sensing device worn by a user, the body worn sensing device comprising at least a photoplethysmography (PPG) sensor. In some embodiments, the body-worn sensing device comprises one or more earrings. In some embodiments, the body-worn sensing device comprises two earrings. The body-worn sensing device may correspond to a wearable device as described with respect to FIG. 1 - FIG. 6. The body-worn sensing device at least comprises a PPG sensor. In an embodiment, each of the one or more earrings comprise one or more of: a temperature sensor, an accelerometer, or a gyroscope. In an embodiment, the temperature sensor comprises a created cavity temperature (CCT) sensor, an ambient temperature sensor, or both.

[0149] At block 804, processing logic determines, based on the first biometric data, a first heart rate variability (HRV) of the user. As described, HRV is a metric that quantifies the variation in the user's heartbeat. In some embodiments, determining the first HRV based on the first biometric data comprises determining RR intervals in the first biometric data and determining a variation of the RR intervals over a period of time. In some embodiments, the variation is determined by determining or calculating a Root Mean Square of Successive Differences (RMSSD) or a Standard Deviation of NN intervals (SDNN) of the RR intervals over the period of time. [0150] At block 806, processing logic determines, based on at least on the first biometric data, a daily threshold associated with a first state and a second state of the user for a day. The first state corresponds to a stress accumulation of the user (e.g., an activated state) and the second state corresponds to an increase of parasympathetic activity of the user (e.g., a recovery state). In some embodiments, the daily threshold is held constant throughout the day. In other embodiments, the daily threshold may be adjusted (e.g., increased or decreased) during the day, as described in other sections.

[0151] In some embodiments, the first biometric data is collected within a threshold period of time from the user waking from sleep. As such, the resulting first HRV and daily threshold represent a well-rested and low-stress state of the user, thus serving as a baseline for the daily threshold. In some examples, the daily threshold is set as the first HRV, or as the first HRV with one or more adjustments. For example, the daily threshold may be determined as: daily threshold = first HRV + X, where X represents the predetermined constant. In another example, the daily threshold may be determined based on the predetermined constant and a dynamic factor Y, such as daily threshold = first HRV + X + Y. X and Y can be positive or negative, representing an increase or decrease to the baseline first HRV. Y may represent dynamic factors such as a detected length of sleep of the user, a detected menstrual cycle of the user, a historical HRV of the user, an average HRV from a general population, a sensed ambient temperature, a user temperature (e.g., from a CCT sensor), or other factor, as described in other sections. An increase to the daily threshold signals an increased vulnerability of the user to stress as this increase raises the bar for the user to enter the recovery state, and a decrease to the daily threshold signals an increased resiliency of the user.

[0152] In some embodiments, the daily threshold may be dynamically adjusted during the day, depending on the detected ambient temperature (e.g., from an ambient temperature sensor of the body-worn sensing device), the detected user temperature (e.g., from a CCT sensor of the body-worn sensing device) or both. For example, if the ambient temperature or the detected user temperature are above a threshold, then the daily threshold may be adjusted higher to reflect the increased vulnerability of the user to stress. Additionally, or alternatively, the daily threshold may be adjusted based on a time of day, for example, within a threshold time of a target sleep time, the daily threshold may be adjusted higher, either gradually or as a step, to guide the user towards a reduction in activity and increased HRV, prior to sleep.

[0153] At block 808, processing logic receives subsequent biometric data from the body-worn sensing device during the day. In some embodiments, subsequent biometric data may be received periodically, or on an event-driven basis, or both, throughout the day. In some embodiments, processing logic is configured to communicate with and receive biometric data from the body-worn sensing device periodically, and each batch of biometric data received may comprise signals from one or more sensors on the body- worn sensing device. Processing logic may decode these signals and analyze them to determine the HRV, body temperature, ambient temperature, etc., each time they are received.

[0154] At block 810, processing logic determines, based on the subsequent biometric data, a second HRV of the user. This second HRV may be detected throughout the day, to periodically or constantly measure the user's state with respect to the daily threshold. In some embodiments, processing logic sets the daily threshold, the first HRV, or the second HRV, by applying one or more machine learning models. The biometric data contains PPG signal data, as well as other data such as, for example, temperature data, movement (e.g., accelerometer or gyroscope), a respiratory rate, etc.

[0155] At block 812, processing logic determines that the user is in the first state or the second state based on the second HRV and the daily threshold. For example, processing logic compares the second HRV with the daily threshold, and if the second HRV is greater than the daily threshold, processing logic determines that the user is in the first state. If the second HRV is less than the daily threshold, processing logic determines that the user is in the second state.

[0156] At block 814, processing logic outputs through a human machine interface (HMI), an indication that the user is in the first state or the second state. In some embodiments, the HMI comprises a display. Additionally, or alternatively, the HMI may comprise loudspeakers. Outputting the indication may comprise generating and displaying a graphical indication of one or more transitions between the first state and the second state of the user during the day (e.g., a graph or other combination of visual elements). In some embodiments, processing logic outputs, through the HMI, a notification to transition to or maintain the first state, based on the second HRV of the user. For example, a notification may display a text-based notification of “You are entering an active state”. In some embodiments, this notification may include an indication of a target sleep time, for example, “You are entering an active state, and it is nearly time to sleep”. In some embodiments, processing logic may output the indication to the user comprises determining a time of the day, and in response to the time of the day being within a first range, refraining from outputting the indication to the user.

[0157] In some embodiments, processing logic may generate a different indication depending on a detected state of the user. Processing logic may classify the user's state based on a range that the user's HRV falls in, and in accordance with other factors such as, for example, the time of day, menstrual cycle, sleep quality of the previous night, historical HRV patterns of the user, or HRV of general population. Classification states may include, for example, an activated state (first state), a recovery state (second state), or a deep recovery state (e.g., a sub state within the second state). The deep recovery state where the user is recovering at an accelerated pace. This typically follows high- impact recovery activities - processing logic may provide insight into what most effectively restores the user’s physiological balance. In some embodiments, processing logic measures the rate at which the user recovers after experiencing different types of physiological stress. Processing logic may store and present insights into recovery efficiency and tailor personalized recovery strategies based on stressor type.

[0158] In some embodiments, receiving the subsequent biometric data, performing the second HRV, and determining whether the second HRV satisfies the daily threshold is performed is determined at periodic times. Processing logic may apply one or more machine learning models to update values of the second HRV or the daily threshold between the periodic times. In such a manner, even assuming that processing logic receives biometric data from the body-worn sensing device every 15 minutes, or every 30 minutes, processing logic can extrapolate the data points between these periods and provide more seamless monitoring of the user and user HRV.

[0159] In some embodiments, processing logic repeats blocks 808, block 810, and/or block 812 periodically throughout the day. For example, processing logic may perform block 808, block 810, and block 812 each time that the subsequent biometric data is received from the body-worn sensing device. In some embodiments, block 812 may be performed conditionally, for example, when there user's state transitions from the first state to the second state, or from the second state to the first state, or both.

[0160] FIG. 9 shows a flow diagram of an example method, performed by a body-worn sensing device, in accordance with some embodiments. The method 900 may be performed by processing logic of the body-worn sensing device. Processing logic may comprise hardware (circuitry, dedicated logic, etc.), software (machine executable instructions stored in computer readable memory), firmware, or a combination.

[0161] Method 900 may correspond to operations performed by a computing device or body-worn sensing device, as described in other sections, such as, for example, FIG. 1 - FIG. 8.

[0162] At block 902, the body-worn sensing device generates first biometric data with one or more sensors comprising at least a PPG sensor. In some embodiments, body- worn sensing device comprises a first earring and a second earring. In some embodiments, the one or more sensors comprises a created cavity temperature (COT) sensor that is thermally coupled to a post of the first earring and of the second earring, as described further in other sections. Additionally, or alternatively, the one or more sensors comprises an ambient temperature sensor that is not thermally coupled to the post. In some embodiments, the one or more sensors further comprise an accelerometer, a gyroscope, or both.

[0163] At block 904, the body-worn sensing device transmits the first biometric data to the computing device, wherein the computing device is configured to determine, based on the first biometric data, a first heart rate variability (HRV) of the user, and to set, based on at least on the first HRV, a daily threshold associated with a first state and a second state of the user for a day. The first biometric data may comprise a signal generated by each of the one or more sensors. The body-worn sensing device may digitize, multiplex, and transmit these signals to the computing device through a wireless transmitter of the body-worn sensing device. This first biometric data may be generated during a morning period, which may correspond to a time period during or after the user wakes up.

[0164] At block 906, the body-worn sensing device transmits subsequent biometric data to the computing device during the day, wherein the computing device is further configured to determine, based on the subsequent biometric data, a second HRV of the user, to determine that the user is in the first state or the second state based on the second HRV and the daily threshold, and to output through a human machine interface (HMI), an indication that the user is in the first state or the second state.

[0165] The body-worn sensing device may sense biometric data of the user throughout the day, digitize and package it into the subsequent biometric data, and transmit this to the computing device on a periodic basis throughout the day, for example, every 15 minutes, every 30 minutes, or other period.

[0166] In some embodiments, the computing device and the body-worn sensing device are combined. In such a case, the body-worn sensing device may perform all the processing operations of the computing device. In some examples, the body-worn sensing device may comprise an HMI such as, for example, a head mounted display, and/or speakers (e.g., ear-worn speakers).

[0167] FIG. 10 illustrates an example of a computing device 1000, in accordance with some aspects. Computing device 1000 may correspond to a computing device as described in other sections, for example, as described with respect to FIG. 1 - FIG. 9. In some examples, computing device 1000 may correspond to an electronic device such as, for example, a desktop computer, a tablet computer, a smart phone, a computer laptop, a smart speaker, a media player, a household appliance, a headphone set, a head mounted display (HMD), smart glasses, an infotainment system for an automobile or other vehicle, a body-worn sensing device, or other computing device. The computing device 1000 can be configured to perform the method and processes described in the present disclosure. This example is not intended to represent any particular architecture or manner of interconnecting the components as such details are not germane to the aspects herein. It will also be appreciated that other types of computing systems that have fewer or more components than shown can also be used. Accordingly, the processes described herein are not limited to use with the hardware and software shown. [0168] The computing device can include one or more buses 1016 that serve to interconnect the various components of the system. One or more processors 1002 are coupled to bus as is known in the art. The processor(s) may be microprocessors or special purpose processors, system on chip (SOC), a central processing unit, a graphics processing unit, a processor created through an Application Specific Integrated Circuit (ASIC), or combinations thereof. Memory 1008 can include Read Only Memory (ROM), volatile memory, and non-volatile memory, or combinations thereof, coupled to the bus using techniques known in the art. Sensors 1014 can include an accelerometer, an inertial measurement unit (IMU) and/or one or more cameras (e.g., RGB camera, RGBD camera, depth camera, etc.) or other sensors described herein. The computing device can further include a display 1012 (e.g., an HMD, or touchscreen display).

[0169] Memory 1008 can be connected to the bus and can include DRAM, a hard disk drive or a flash memory or a magnetic optical drive or magnetic memory or an optical drive or other types of memory systems that maintain data even after power is removed from the system. In one aspect, the processor 1002 retrieves computer program instructions stored in a machine readable storage medium (memory) and executes those instructions to perform operations described herein.

[0170] Audio hardware, although not shown, can be coupled to the one or more buses in order to receive audio signals to be processed and output by speakers 1006. Audio hardware can include digital to analog and/or analog to digital converters. Audio hardware can also include audio amplifiers and filters. The audio hardware can also interface with microphones 1004 (e.g., microphone arrays) to receive audio signals (whether analog or digital), digitize them when appropriate, and communicate the signals to the bus.

[0171] Communication module 1010 can communicate with remote devices and networks through a wired or wireless interface. For example, communication module can communicate over known technologies such as TCP/IP, Ethernet, Wi-Fi, 3G, 4G, 5G, Bluetooth, ZigBee, or other equivalent technologies. The communication module can include wired or wireless transmitters and receivers that can communicate (e.g., receive and transmit data) with networked devices such as servers (e.g., the cloud) and/or other devices such as remote speakers and remote microphones.

[0172] It will be appreciated that the aspects disclosed herein can utilize memory that is remote from the system, such as a network storage device which is coupled to the system through a network interface such as a modem or Ethernet interface. The buses can be connected to each other through various bridges, controllers and/or adapters as is well known in the art. In one aspect, one or more network device(s) can be coupled to the bus. The network device(s) can be wired network devices (e.g., Ethernet) or wireless network devices (e.g., Wi-Fi, Bluetooth). In some aspects, various aspects described (e.g., simulation, analysis, estimation, modeling, object detection, etc.,) can be performed by a networked server in communication with the capture device.

[0173] Various aspects described herein may be embodied, at least in part, in software. That is, the techniques may be carried out in response to a processor of the computing system executing a sequence of instructions contained in a storage medium, such as a non-transitory machine-readable storage medium (e.g. DRAM or flash memory). In various aspects, hardwired circuitry may be used in combination with software instructions to implement the techniques described herein. Thus the techniques are not limited to any specific combination of hardware circuitry and software, or to any particular source for the instructions executed by the system.

[0174] In the description, certain terminology is used to describe features of various aspects. For example, in certain situations, the terms “logic”, “processor”, “manager”, “renderer”, “system”, “device”, “mapper”, “block”, may be representative of hardware and/or software configured to perform one or more processes or functions. For instance, examples of "hardware" include, but are not limited or restricted to an integrated circuit such as a processor (e.g., a digital signal processor, microprocessor, application specific integrated circuit, a micro-controller, etc.). Thus, different combinations of hardware and/or software can be implemented to perform the processes or functions described by the above terms, as understood by one skilled in the art. Of course, the hardware may be alternatively implemented as a finite state machine or even combinatorial logic. An example of “software” includes executable code in the form of an application, an applet, a routine or even a series of instructions. As mentioned above, the software may be stored in any type of machine-readable medium.

[0175] Some portions of the preceding detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the ways used by those skilled in the signal processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities. It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as those set forth in the claims below, refer to the action and processes of an system, or similar electronic device, that manipulates and transforms data represented as physical (electronic) quantities within the system's registers and memories into other data similarly represented as physical quantities within the system memories or registers or other such information storage, transmission or display devices. [0176] The processes and blocks described herein are not limited to the specific examples described and are not limited to the specific orders used as examples herein. Rather, any of the processing blocks may be re-ordered, combined or removed, performed in parallel or in serial, as desired, to achieve the results set forth above. The processing blocks associated with implementing the computing system may be performed by one or more programmable processors executing one or more computer programs stored on a non-transitory computer readable storage medium to perform the functions of the system. All or part of the computing system may be implemented as, special purpose logic circuitry (e.g., an FPGA (field-programmable gate array) and/or an ASIC (application-specific integrated circuit)). All or part of the computing system may be implemented using electronic hardware circuitry that include electronic devices such as, for example, at least one of a processor, a memory, a programmable logic device or a logic gate. Further, processes can be implemented in any combination hardware devices and software components.

[0177] To aid the Patent Office and any readers of any patent issued on this application in interpreting the claims appended hereto, applicants wish to note that they do not intend any of the appended claims or claim elements to invoke 35 U.S.C. 112(f) unless the words “means for” or “step for” are explicitly used in the particular claim.

[0178] While various embodiments of the present disclosure have been described above, it should be understood that they have been presented by way of example only, and not by way of limitation. Likewise, the various diagrams may depict an example architectural or other configuration for the disclosure, which is done to aid in understanding the features and functionality that can be included in the disclosure. The disclosure is not restricted to the illustrated example architectures or configurations but can be implemented using a variety of alternative architectures and configurations. Additionally, although the disclosure is described above in terms of various exemplary embodiments and implementations, it should be understood that the various features and functionality described in one or more of the individual embodiments are not limited in their applicability to the particular embodiment with which they are described. They instead can be applied, alone or in some combination, to one or more of the other embodiments of the disclosure, whether or not such embodiments are described, and whether or not such features are presented as being a part of a described embodiment. Thus, the breadth and scope of the present disclosure should not be limited by any of the above-described exemplary embodiments.

[0179] Unless otherwise defined, all terms (including technical and scientific terms) are to be given their ordinary and customary meaning to a person of ordinary skill in the art, and are not to be limited to a special or customized meaning unless expressly so defined herein. It should be noted that the use of particular terminology when describing certain features or aspects of the disclosure should not be taken to imply that the terminology is being re-defined herein to be restricted to include any specific characteristics of the features or aspects of the disclosure with which that terminology is associated. Terms and phrases used in this application, and variations thereof, especially in the appended claims, unless otherwise expressly stated, should be construed as open ended as opposed to limiting. As examples of the foregoing, the term ‘including’ should be read to mean ‘including, without limitation,’ ‘including but not limited to,’ or the like; the term ‘comprising’ as used herein is synonymous with ‘including,’ ‘containing,’ or ‘characterized by,’ and is inclusive or open-ended and does not exclude additional, unrecited elements or method steps; the term ‘having’ should be interpreted as ‘having at least;’ the term ‘includes’ should be interpreted as ‘includes but is not limited to;’ the term ‘example’ is used to provide exemplary instances of the item in discussion, not an exhaustive or limiting list thereof; adjectives such as ‘known’, ‘normal’, ‘standard’, and terms of similar meaning should not be construed as limiting the item described to a given time period or to an item available as of a given time, but instead should be read to encompass known, normal, or standard technologies that may be available or known now or at any time in the future; and use of terms like ‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words of similar meaning should not be understood as implying that certain features are critical, essential, or even important to the structure or function of the present disclosure, but instead as merely intended to highlight alternative or additional features that may or may not be utilized in a particular embodiment of the present disclosure. Likewise, a group of items linked with the conjunction ‘and’ should not be read as requiring that each and every one of those items be present in the grouping, but rather should be read as ‘and/or’ unless expressly stated otherwise. Similarly, a group of items linked with the conjunction ‘or’ should not be read as requiring mutual exclusivity among that group, but rather should be read as ‘and/or’ unless expressly stated otherwise.

[0180] Where a range of values is provided, it is understood that the upper and lower limit, and each intervening value between the upper and lower limit of the range is encompassed within the embodiments. For instance, when a plurality of ranges are provided, any combination of a minimum value and a maximum value described in the plurality of ranges are contemplated by the present disclosure. For example, if ranges of ‘from about 20% to about 80%’ and ‘from about 30% to about 70%’ are described, a range of ‘from about 20% to about 70%’ or a range of ‘from about 30% to about 80%’ are also contemplated by the present disclosure.

[0181] With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity. The indefinite article ‘a’ or ‘an’ does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.

[0182] It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases ‘at least one’ and ‘one or more’ to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles ‘a’ or ‘an’ limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases ‘one or more” or ‘at least one’ and indefinite articles such as ‘a’ or ‘an’ (e.g., ‘a’ and/or ‘an’ should typically be interpreted to mean ‘at least one’ or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of ‘two recitations,’ without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to ‘at least one of A, B, and C, etc.’ is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., ‘a system having at least one of A, B, and C’ would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to ‘at least one of A, B, or C, etc.’ is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., ‘a system having at least one of A, B, or C’ would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase ‘A or B’ will be understood to include the possibilities of ‘A’ or ‘B’ or ‘A and B.’

[0183] All numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification are to be understood as being modified in all instances by the terms ‘about,’ ‘approximately,’ or ‘generally.’ Accordingly, unless indicated to the contrary, the numerical parameters set forth herein are approximations that may vary depending upon the desired properties sought to be obtained. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of any claims in any application claiming priority to the present application, each numerical parameter should be construed in light of the number of significant digits and ordinary rounding approaches. As used herein, the terms ‘about,’ ‘approximately,’ or ‘generally,’ when used to modify a value, indicate that the value can be raised or lowered by 5% and remain within the disclosed embodiment.

[0184] All of the features disclosed in this specification (including any accompanying exhibits, claims, abstract and drawings), and/or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and/or steps are mutually exclusive. The disclosure is not restricted to the details of any foregoing embodiments. The disclosure extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims, abstract and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed.

[0185] While the present subject matter has been described in detail with respect to various specific example embodiments thereof, each example is provided by way of explanation, not limitation of the disclosure. Those skilled in the art, upon attaining an understanding of the foregoing, can readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, the subject disclosure does not preclude inclusion of such modifications, variations and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the present disclosure covers such alterations, variations, and equivalents.

[0186] It should be understood that biometric data collection may include sensitive and confidential information relating to a wearer, such as but not limited to genetic predispositions, cardiovascular health, pregnancy, etc. Accordingly, and further to the descriptions above, some or all data acquired using the products or end products of the aforementioned systems and methods will be kept private and confidential for each specific wearer unless the wearer agrees to share such data. Thus, it should not be construed that any information discovered or inferred from the use of the products or end products produced using the aforementioned systems and methods will be improperly used or published. For example, information acquired from the wearable devices described herein may be treated so that no person without express or implied consent is capable of accessing said information. Thus, the information acquired from the wearable device may be kept confidential and access to the information may be controlled exclusively by the wearer of the wearable device whose information is determined by using the wearable device.

Claims

CLAIMS What is claimed is:

1. A method, performed by a computing device, comprising: receiving first biometric data from a body-worn sensing device worn by a user, the body worn sensing device comprising at least a photoplethysmography (PPG) sensor; determining, based on the first biometric data, a first heart rate variability (HRV) of the user; setting, based on at least on the first HRV, a daily threshold associated with a first state and second state of the user for a day; receiving subsequent biometric data from the body-worn sensing device during the day; determining, based on the subsequent biometric data, a second HRV of the user; and determining that the user is in the first state or the second state based on the second HRV and the daily threshold; and outputting through a human machine interface (HMI), an indication that the user is in the first state or the second state.

2. The method of claim 1 , wherein the first state corresponds to a stress accumulation of the user and the second state corresponds to a parasympathetic dominance of the user.

3. The method of claim 1 , wherein the first biometric data is collected within a threshold period of time from the user waking from sleep, and wherein setting the daily threshold comprises setting the daily threshold as the first HRV or as the first HRV with one or more adjustments.

4. The method of claim 3, wherein the one or more adjustments to the first HRV comprises: an increase or decrease to the first HRV based on a sleep quality metric of the user.

5. The method of claim 3, wherein the one or more adjustments to the first HRV comprises: an increase or decrease to the first HRV based on a historical HRV of the user, an average HRV from a general population, or both.

6. The method of claim 3, wherein the one or more adjustments to the first HRV comprises: an increase or decrease to the first HRV based on an ambient temperature, a user temperature, or both.

7. The method of claim 1 , further comprising increasing or decreasing the daily threshold during the day in response to one or more of: detected activity of the user, a detected menstrual cycle of the user, the subsequent biometric data, an ambient temperature, a user temperature, a time of the day, a historical behavior of the user, or a location of the user.

8. The method of claim 1 , wherein the HMI comprises a display, and outputting the indication comprises generating and displaying a graphical indication of one or more transitions between the first state and the second state of the user during the day.

9. The method of claim 1 , further comprising: outputting, through the HMI, a notification to transition to or maintain the first state, based on the second HRV of the user.

10. The method of claim 1 , wherein outputting the indication to the user comprises determining a time of the day, and in response to the time of the day being within a first range, refraining from outputting the indication to the user.

11 . The method of claim 1 , wherein the setting of the daily threshold, the first HRV, or the second HRV, are performed by applying one or more machine learning models.

12. The method of claim 1 , wherein receiving the subsequent biometric data, performing the second HRV, and determining whether the second HRV satisfies the daily threshold is performed at periodic times, and wherein a machine learning model is applied to update values of the second HRV or the daily threshold between the periodic times.

13. The method of claim 12, wherein the body-worn sensing device comprises one or more earrings, each comprising one or more of: a temperature sensor, an accelerometer, or a gyroscope.

14. The method of claim 13, wherein the temperature sensor comprises a created cavity temperature (CCT) sensor, an ambient temperature sensor, or both.

15. The method of claim 1 , wherein determining the first HRV based on the first biometric data comprises determining RR intervals in the first biometric data and determining a variation of the RR intervals over a period of time.

16. The method of claim 15, wherein the variation is determined by applying a Root Mean Square of Successive Differences (RMSSD) or a Standard Deviation of NN intervals (SDNN) of the RR intervals over the period of time.

17. A method, performed by a body-worn sensing device, comprising: generating first biometric data with one or more sensors comprising at least a photoplethysmography (PPG) sensor; transmitting, to a computing device, the first biometric data, wherein the computing device is configured to determine, based on the first biometric data, a first heart rate variability (HRV) of the user, and to set, based on at least on the first HRV, a daily threshold associated with a first state and a second state of the user for a day; and transmitting, to the computing device, subsequent biometric data to the computing device during the day, wherein the computing device is further configured to determine, based on the subsequent biometric data, a second HRV of the user, to determine that the user is in the first state or the second state based on the second HRV and the daily threshold, and to output through a human machine interface (HMI), an indication that the user is in the first state or the second state.

18. The method of claim 17, wherein the body-worn sensing device comprises a first earring and a second earring.

19. The method of claim 18, wherein the one or more sensors comprises a created cavity temperature (CCT) sensor that is thermally coupled to a post of the first earring and of the second earring.

20. The method of claim 19, wherein the one or more sensors comprises an ambient temperature sensor that is not thermally coupled to the post.

21. The method of claim 18, wherein the one or more sensors further comprise an accelerometer, a gyroscope, or both.

22. A computing device, comprising one or more processors, coupled to memory storing instructions that, when executed by the one or more processors, causes the computing device to perform the method of any one of claims 1 to 16 or the method of any one of claims 17 to 21.

23. A computer-readable storage medium, storing instructions that, when executed by one or more processors of a computing device, causes the computing device to perform the method of any one of claims 1 to 16 or the method of any one of claims 17 to 21.