CN113274008B - A three-wavelength blood oxygen saturation monitoring method based on wearable devices - Google Patents
A three-wavelength blood oxygen saturation monitoring method based on wearable devices Download PDFInfo
- Publication number
- CN113274008B CN113274008B CN202110428234.6A CN202110428234A CN113274008B CN 113274008 B CN113274008 B CN 113274008B CN 202110428234 A CN202110428234 A CN 202110428234A CN 113274008 B CN113274008 B CN 113274008B
- Authority
- CN
- China
- Prior art keywords
- pulse wave
- signal
- green light
- detected
- matrix
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000008280 blood Substances 0.000 title claims abstract description 41
- 210000004369 blood Anatomy 0.000 title claims abstract description 41
- 238000000034 method Methods 0.000 title claims abstract description 41
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 title claims abstract description 40
- 239000001301 oxygen Substances 0.000 title claims abstract description 40
- 229910052760 oxygen Inorganic materials 0.000 title claims abstract description 40
- 238000012544 monitoring process Methods 0.000 title claims abstract description 22
- 238000001914 filtration Methods 0.000 claims abstract description 126
- 230000007246 mechanism Effects 0.000 claims abstract description 51
- 239000011159 matrix material Substances 0.000 claims description 109
- 238000001228 spectrum Methods 0.000 claims description 29
- 230000006870 function Effects 0.000 claims description 26
- 230000003068 static effect Effects 0.000 claims description 26
- 230000009466 transformation Effects 0.000 claims description 12
- 230000003287 optical effect Effects 0.000 claims description 11
- 230000009471 action Effects 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 8
- 238000013186 photoplethysmography Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000003044 adaptive effect Effects 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 description 9
- 238000004590 computer program Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 230000033764 rhythmic process Effects 0.000 description 6
- 102000001554 Hemoglobins Human genes 0.000 description 5
- 108010054147 Hemoglobins Proteins 0.000 description 5
- 238000001514 detection method Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 2
- 238000010241 blood sampling Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000014541 detection of hypoxia Effects 0.000 description 1
- 238000000840 electrochemical analysis Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 238000001727 in vivo Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 210000004072 lung Anatomy 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 238000010025 steaming Methods 0.000 description 1
- 210000000707 wrist Anatomy 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
- A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Surgery (AREA)
- Medical Informatics (AREA)
- Veterinary Medicine (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Molecular Biology (AREA)
- Artificial Intelligence (AREA)
- Psychiatry (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physiology (AREA)
- Signal Processing (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Optics & Photonics (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention provides a three-wavelength blood oxygen saturation monitoring method based on wearable equipment, which comprises the following steps: acquiring pulse wave signals of an object to be detected; the pulse wave signals comprise a green light pulse wave signal, a red light pulse wave signal and an infrared light pulse wave signal; acquiring a state filtering range of a green light pulse wave signal based on the environment of an object to be detected, and generating a green light filtering mechanism according to the state filtering range; searching a red light characteristic point according to the red light pulse wave signal, and determining a first signal component based on the red light characteristic point; searching infrared light characteristic points according to the infrared light pulse wave signals, and determining a second signal component based on the infrared light characteristic points; filtering the first signal component and the second signal component through a green light filtering mechanism, and establishing a dynamic self-adaptive model after the processing; and determining the blood oxygen saturation of the object to be detected based on the dynamic self-adaptive model.
Description
Technical Field
The invention relates to the technical field of blood oxygen saturation monitoring, in particular to a three-wavelength blood oxygen saturation monitoring method based on wearable equipment.
Background
Blood sample saturation reflects the percentage of oxygen-bound hemoglobin in the blood over all hemoglobin, i.e., monitoring blood oxygen saturation is an estimate of lung oxygen-bound hemoglobin, an important means and method for clinical detection of hypoxia.
The traditional blood oxygen saturation measuring method is characterized in that a blood gas analyzer is used for blood sampling of a human body, then electrochemical analysis is carried out on a blood sampling sample, partial pressure of blood oxygen is measured, and the blood oxygen saturation is calculated. The existing wrist technology adopts a finger-sleeve type photoelectric sensor, uses red light with the wavelength of 660nm and near infrared light with the wavelength of 940nm as incident light sources, uses fingers as a transparent container for containing hemoglobin, and measures the light transmission intensity through a tissue bed to calculate the concentration of the hemoglobin and the blood oxygen saturation.
The prior art calculates the wavelength coefficient by using single red light and infrared light signals, then solves the blood oxygen saturation by using an empirical formula, is influenced by different states of the environment and the user, has strong interference, is uncomfortable to wear and has poor robustness, and the prior art has the problem that the blood oxygen saturation cannot be accurately measured due to the reasons of self-adaption capability, single wavelength coefficient and the like.
Disclosure of Invention
The invention provides a three-wavelength blood oxygen saturation monitoring method based on wearable equipment, which aims to solve the problems in the background technology.
The invention provides a three-wavelength blood oxygen saturation monitoring method based on wearable equipment, which is characterized by comprising the following steps of:
acquiring pulse wave signals of an object to be detected; wherein,
The pulse wave signals comprise a green light pulse wave signal, a red light pulse wave signal and an infrared light pulse wave signal;
acquiring a state filtering range of a green light pulse wave signal based on the environment of an object to be detected, and generating a green light filtering mechanism according to the state filtering range;
Searching a red light characteristic point according to the red light pulse wave signal, and determining a first signal component based on the red light characteristic point;
searching infrared light characteristic points according to the infrared light pulse wave signals, and determining a second signal component based on the infrared light characteristic points;
Filtering the first signal component and the second signal component through a green light filtering mechanism, and establishing a dynamic self-adaptive model after the processing;
And calculating the blood oxygen saturation of the object to be detected based on the dynamic self-adaptive model.
Preferably, before the step of acquiring the pulse wave signal of the object to be detected, the method further includes:
Based on a preset sensing device in the wearable equipment, judging whether an object to be detected is in a static state or not:
When detecting that the object to be detected is in a static state, automatically issuing an acquisition instruction based on preset wearing equipment, and acquiring a red light pulse wave signal and an infrared light pulse signal of the object to be detected;
when detecting that the object to be detected is not in a static state, automatically issuing an acquisition instruction based on preset wearing equipment, and acquiring a green light pulse wave signal of the object to be detected.
Preferably, the acquiring the pulse wave signal when the object to be detected is in the stationary state includes:
based on a preset wearable device, alternately emitting light waves to an object to be detected in a static state at regular time; wherein,
The light wave rays comprise green light rays, red light rays and infrared light rays;
acquiring the luminous intensity of the red light ray and the infrared light ray, and determining the arterial pulse optical path of an object to be detected according to the luminous intensity;
Recording the pulse intensity of the object to be detected in real time according to the arterial pulse optical path;
and determining a pulse wave signal of the object to be detected according to the pulse intensity.
Preferably, the step of obtaining a state filtering range of the green pulse wave signal based on the environment of the object to be detected, and generating a green filtering mechanism according to the state filtering range includes:
Acquiring green light pulse wave signals of an object to be detected at fixed time, and determining characteristic frequencies of the green light pulse wave signals according to the green light pulse wave signals;
acquiring signal influence parameters of pulse wave signals according to the characteristic frequency; wherein,
The signal influence parameters comprise a fluctuation coefficient, a cut-off frequency and a low-pass impedance order of the pulse wave signal;
Calculating maximum attenuation data according to the signal influence parameters; wherein,
The loss data are based on the loss data under the influence of the self parameters of the wearable equipment;
And transmitting the maximum attenuation data to a preset chebyshev filter based on the environment of the object to be detected, and establishing a green light filtering mechanism.
Preferably, the transmitting the maximum attenuation data to a preset chebyshev filter based on the environment of the object to be detected, and establishing a green light filtering mechanism includes:
based on the environment of an object to be detected, acquiring green light filtering frequency through a preset chebyshev filter; wherein,
The filtering frequency is noise frequency and abnormal signals of the object to be detected in the environment;
Calculating a pulse wave characteristic matrix of the object to be detected in a non-stationary state;
Acquiring and transmitting maximum attenuation data to a chebyshev filter, filtering the pulse wave feature matrix through green light filtering frequency, and determining a target filtering matrix;
and establishing a green light filtering mechanism according to the target filtering matrix.
Preferably, the calculating the pulse wave feature matrix of the object to be detected in the non-stationary state includes:
When the object to be detected is in a non-stationary state, collecting green light pulse wave signals of the object to be detected;
According to the green light pulse wave signals, determining green light pulse wave characteristic points;
fitting and calculating the pulse wave feature points, and determining feature coefficients of the green light pulse feature points;
And according to the characteristic coefficients, matrixing the pulse wave characteristic points to determine a pulse wave characteristic matrix.
Preferably, the establishing a green light filtering mechanism according to the target filtering matrix includes:
Step 1: acquiring a preset ideal filter matrix
Wherein, density represents ideal filter matrix, n×n represents matrix of n rows and n columns of ideal filter matrix, density represents pulse wave characteristic data under ideal filter condition in ideal filter matrix;
step 2: obtaining a target filter matrix
Wherein, target represents the target filter matrix, n×n represents the matrix of n rows and n columns of the target filter matrix, and target represents the pulse wave characteristic data under the condition of target filtering in the target filter matrix;
step 3: determining a transformation coefficient matrix according to the target filter matrix and the ideal filter matrix;
wherein W n×n represents a transformation coefficient matrix, and E n×n represents an identity matrix;
step 4: establishing a green light filtering spectrum matrix through a transformation coefficient matrix;
wherein r represents a green light filtering spectrum matrix;
Step 5: and determining a green light filtering mechanism according to the green light filtering spectrum matrix.
Preferably, the searching for a red light feature point according to the red light pulse wave signal, and determining a first signal component based on the red light feature point includes:
Calculating the intensity of the red pulse wave according to the red pulse wave signal;
intensity sorting is carried out on the red light pulse wave intensities, and a first intensity peak value is determined;
collecting and reconstructing red light characteristic points through the first intensity peak value;
According to the red light characteristic points, a red light direct current component and a red light alternating current component are calculated; wherein,
The red light alternating current component is used for obtaining a change signal of arterial pulse based on photoplethysmography under the action of red light;
And determining a first signal component according to the red direct current component and the red alternating current component.
Preferably, the searching for an infrared light feature point according to the infrared light pulse wave signal, and determining a second signal component based on the infrared light feature point, includes:
Calculating the infrared light pulse wave intensity according to the infrared light pulse wave signal;
intensity sorting is carried out on the infrared light pulse wave intensities, and a second intensity peak value is determined;
collecting and reconstructing infrared light characteristic points through the second intensity peak value;
According to the infrared light external characteristic points, an infrared light direct current component and an infrared light alternating current component are calculated; wherein,
The infrared light alternating current component is a change signal of arterial pulse obtained based on photoplethysmography under the action of the infrared light;
and determining a second signal component according to the infrared light direct current component and the infrared light alternating current component.
Preferably, the filtering processing is performed on the first signal component and the second signal component by a green light filtering mechanism, and a dynamic adaptive model is built after the processing, including:
Step S1, collecting a first signal component delta sig 1(fd1,fs1) and a second signal component delta sig 2(fd2,fs2);
Wherein Δsig 1 represents the first signal component, f d1 represents the first dc component of the first signal component, f s1 represents the first ac component of the first signal component, Δsig 2 represents the second signal component, f d2 represents the second dc component of the second signal component, and f s2 represents the second ac component of the second signal component;
Step S2: acquiring a green light filtering spectrum matrix r of a green light filtering mechanism, performing cross spectrum calculation on the first signal component delta sig 1(fd1,fs1) and the second signal component delta sig 2(fd2,fs2), and determining a cross signal;
Wherein S n (sig) represents a cross signal, E represents an identity matrix which is multiplied by a green light filtering spectrum matrix, i represents ordinal numbers of an ith signal component collected in the first signal component and the second signal component, n represents a first signal component and the second signal component collected together, and i epsilon (1, n);
step S3: transmitting the cross signal to the green light filtering mechanism to obtain a dynamic self-adaptive model function;
wherein, the fer dynamic self-adaptive model function, T represents all the acquired signal periods, T represents one complete signal period in the complete signal period, Representing a dynamic self-adaptive function, wherein θ represents the period offset of different signals based on the dynamic state under the self-adaptive condition, and q represents the number of the period offsets of different signals acquired;
Step S4: when the fer dynamic self-adaptive model function cannot be synthesized, the wearable device automatically reports errors;
step S5: and when the fer dynamic self-adaptive model function is synthesized, establishing a dynamic self-adaptive model through the dynamic self-adaptive model function.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
Fig. 1 is a flowchart of a three-wavelength blood oxygen saturation monitoring method based on a wearable device in an embodiment of the invention;
FIG. 2 is a flowchart of a three-wavelength blood oxygen saturation monitoring method based on a wearable device in an embodiment of the invention;
fig. 3 is a flowchart of a three-wavelength blood oxygen saturation monitoring method based on a wearable device in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Example 1:
According to fig. 1, the invention provides a three-wavelength blood oxygen saturation monitoring method based on a wearable device, which is characterized by comprising the following steps:
acquiring pulse wave signals of an object to be detected; wherein,
The pulse wave signals comprise a green light pulse wave signal, a red light pulse wave signal and an infrared light pulse wave signal;
acquiring a state filtering range of a green light pulse wave signal based on the environment of an object to be detected, and generating a green light filtering mechanism according to the state filtering range;
Searching a red light characteristic point according to the red light pulse wave signal, and determining a first signal component based on the red light characteristic point;
searching infrared light characteristic points according to the infrared light pulse wave signals, and determining a second signal component based on the infrared light characteristic points;
Filtering the first signal component and the second signal component through a green light filtering mechanism, and establishing a dynamic self-adaptive model after the processing;
And calculating the blood oxygen saturation of the object to be detected based on the dynamic self-adaptive model.
The working principle of the technical scheme is as follows:
the embodiment of the invention provides a three-wavelength blood oxygen saturation monitoring method based on wearable equipment, which comprises the steps of obtaining a green light pulse wave signal of a user by utilizing a watch to send an acquisition instruction when the user is in a static state, obtaining a red light pulse wave signal and an infrared light pulse wave signal of the user, and obtaining a pulse wave signal when an object to be detected is in the static state; the pulse wave signals comprise a green light pulse wave signal, a red light pulse wave signal and an infrared light pulse wave signal; then, determining a proper filtering method according to the green light, carrying out spectrum analysis on the green light, determining a filtering frequency range suitable for the current state and the environment, searching a red light characteristic point according to the red light pulse wave signal according to the filtering range of the green light, and determining a first signal component based on the red light characteristic point; searching infrared light characteristic points according to the infrared light pulse wave signals, and determining a second signal component based on the infrared light characteristic points; processing red light and infrared light pulse wave signals, then calculating a red light direct current component and a red light alternating current component, finding out red light characteristic points, calculating a red light direct current component and a red light alternating current component according to a preset formula, then calculating an infrared light direct current component and an infrared light alternating current component, finding out infrared light characteristic points, calculating an infrared light direct current component and an infrared light alternating current component according to the formula, calculating a blood oxygen saturation, calculating a wavelength coefficient ratio of a calculation result, calculating the blood oxygen saturation by using a unitary one-time equation, and using a traditional wearable device to obtain a green light signal with better quality than the red light signal and the infrared light signal, wherein the traditional wearable device can be used for establishing a proper filtering spectrum range and a proper method, and the filtering spectrum range is closely related to the current environment and state, so the method has certain self-adaption, and the whole process generates a green light filtering mechanism by obtaining a state filtering range of the green light pulse wave signal according to the state filtering range; based on the green light filtering mechanism, calculating a wavelength coefficient ratio through the first signal component and the second signal component, and determining the blood oxygen saturation of the object to be detected according to the wavelength coefficient ratio.
The beneficial effects of the technical scheme are as follows:
According to the technical scheme, the whole filtering spectrum range of the current environment and state is obtained based on the self-adaptability of green light, and pulse signals of the user based on a green light filtering mechanism are obtained under the irradiation of pulse red light and infrared light of the user, so that the blood oxygen concentration is determined, and the health of the user is ensured.
Example 2:
the present technical solution provides an embodiment, before obtaining the pulse wave signal of the object to be detected, the method further includes:
Based on a preset sensing device in the wearable equipment, judging whether an object to be detected is in a static state or not:
When detecting that the object to be detected is in a static state, automatically issuing an acquisition instruction based on preset wearing equipment, and acquiring a red light pulse wave signal and an infrared light pulse signal of the object to be detected;
when detecting that the object to be detected is not in a static state, automatically issuing an acquisition instruction based on preset wearing equipment, and acquiring a green light pulse wave signal of the object to be detected.
The working principle of the technical scheme is as follows:
the technical scheme provides an embodiment, before acquiring a pulse wave signal when an object to be detected is in a static state, the method further comprises judging whether the object to be detected is in the static state, and because pulses of people in different states and environments are different and have different influences, green light filtering values of the object to be detected need to be firstly acquired, namely green light pulse signals of users in large environments and the static state, then when the object to be detected is detected to be in the static state, based on preset wearing equipment, an acquisition instruction is automatically issued, and the pulse wave signal of the object to be detected is acquired; when detecting that the object to be detected is not in a static state, the wearable device automatically enters a power saving mode.
The beneficial effects of the technical scheme are as follows:
When the green light filtering value is monitored in the user static state, the technical scheme has an acquisition operation on the frequency spectrum of the environment and is used for self-adapting to the surrounding environment frequency spectrum, so that the acquired pulse signal data is more accurate and more suitable for the current environment state.
Example 3:
The present technical solution provides an embodiment, the obtaining a pulse wave signal when an object to be detected is in a stationary state includes:
based on a preset wearable device, alternately emitting light waves to an object to be detected in a static state at regular time; wherein,
The light wave rays comprise green light rays, red light rays and infrared light rays;
acquiring the luminous intensity of the red light ray and the infrared light ray, and determining the arterial pulse optical path of an object to be detected according to the luminous intensity;
Recording the pulse intensity of the object to be detected in real time according to the arterial pulse optical path;
and determining a pulse wave signal of the object to be detected according to the pulse intensity.
The working principle of the technical scheme is as follows:
According to the technical scheme, through the comparison between the acquired detection object in the static state and the non-static state, a great amount of data identification is carried out, so that pulse wave signals are acquired when the detection object is in the static state, and based on the preset wearable equipment, light wave rays are alternately emitted to the object to be detected when the detection object is in the static state at regular time; wherein the light wave rays comprise green light rays, red light rays and infrared light rays; acquiring the luminous intensity of the red light ray and the infrared light ray, and determining the arterial pulse optical path of an object to be detected according to the luminous intensity; recording the pulse intensity of the object to be detected in real time according to the arterial pulse optical path; according to the pulse intensity, determining pulse wave signals of an object to be detected, measuring the pulse of a user through three different light rays, thereby acquiring the pulse signals of the user in different environment states, acquiring and analyzing the frequency spectrum of the surrounding environment, and determining the accurate pulse signals.
The beneficial effects of the technical scheme are as follows:
according to the technical scheme, the self-adaptive green light filtering mechanism is established according to the user state and the surrounding environment by acquiring the green light, so that the acquisition of the pulse signals of the user is more suitable for different occasions, different states and different places, and the acquired data is more accurate.
Example 4:
According to fig. 2, the present technical solution provides an embodiment, based on the environment of the object to be detected, a state filtering range of a green pulse wave signal is obtained, and a green filtering mechanism is generated according to the state filtering range, including:
Acquiring green light pulse wave signals of an object to be detected at fixed time, and determining characteristic frequencies of the green light pulse wave signals according to the green light pulse wave signals;
acquiring signal influence parameters of pulse wave signals according to the characteristic frequency; wherein,
The signal influence parameters comprise a fluctuation coefficient, a cut-off frequency and a low-pass impedance order of the pulse wave signal;
Calculating maximum attenuation data according to the signal influence parameters; wherein,
The loss data are based on the loss data under the influence of the self parameters of the wearable equipment;
And transmitting the maximum attenuation data to a preset chebyshev filter based on the environment of the object to be detected, and establishing a green light filtering mechanism.
The working principle of the technical scheme is as follows:
The technical scheme provides an embodiment, and the operation process when the pulse wave signal of the object to be detected is obtained when the object to be detected is in a static state is that light wave rays are alternately emitted to the object to be detected when the object to be detected is in the static state at regular time through the wearable equipment of the technical scheme; wherein the light wave rays comprise green light rays, red light rays and infrared light rays; acquiring the luminous intensity of the red light ray and the infrared light ray, wherein the luminous intensity is based on the tissue thickness of an object to be detected and the concentration of in-vivo liquid, the incident intensity of the light ray is determined through the self condition of the object to be detected and the light ray propagation speed, and the arterial pulse optical path of the object to be detected is determined according to the luminous intensity; recording the pulse intensity of the object to be detected in real time according to the arterial pulse optical path; according to the pulse intensity, determining a pulse wave signal of an object to be detected, wherein the pulse wave signal is the most original pulse signal, and the self-adaptive frequency spectrum is generated aiming at the environment and the state of a user, such as the fierce exercise of a gymnasium or the acquisition of the information of the environment in a special environment of steaming and taking a sauna of the gymnasium.
The beneficial effects of the technical scheme are as follows:
The technical scheme provides a self-adaptive green light filtering mechanism, which monitors and collects the state of the environment where the user is located in real time, and self-adapts through autonomous learning to determine a pulse which is more attached to different places.
Example 5:
according to fig. 3, the present technical solution provides an embodiment, where the transmitting the maximum attenuation data to a predetermined chebyshev filter based on the environment of the object to be detected, and establishing a green light filtering mechanism includes:
based on the environment of an object to be detected, acquiring green light filtering frequency through a preset chebyshev filter; wherein,
The filtering frequency is noise frequency and abnormal signals of the object to be detected in the environment;
Calculating a pulse wave characteristic matrix of the object to be detected in a non-stationary state;
Acquiring and transmitting maximum attenuation data to a chebyshev filter, filtering the pulse wave feature matrix through green light filtering frequency, and determining a target filtering matrix;
and establishing a green light filtering mechanism according to the target filtering matrix.
The working principle of the technical scheme is as follows:
The present technical solution provides an embodiment, where the obtaining a state filtering range of a green light pulse wave signal, and generating a green light filtering mechanism according to the state filtering range, includes: acquiring green light pulse wave signals of an object to be detected at fixed time, and determining characteristic frequencies of the green light pulse wave signals according to the green light pulse wave signals; acquiring signal influence parameters of pulse wave signals according to the characteristic frequency; the signal influence parameters comprise a fluctuation coefficient, a cut-off frequency and a low-pass impedance order of a pulse wave signal; calculating maximum attenuation data of a pass band of the chebyshev filter according to the signal influence parameters; and transmitting the maximum attenuation data to a preset chebyshev filter, and establishing a green light filtering mechanism.
Example 6:
The present technical solution provides an embodiment, calculating a pulse wave feature matrix of an object to be detected in a non-stationary state, including:
When the object to be detected is in a non-stationary state, collecting green light pulse wave signals of the object to be detected;
According to the green light pulse wave signals, determining green light pulse wave characteristic points;
fitting and calculating the pulse wave feature points, and determining feature coefficients of the green light pulse feature points;
And according to the characteristic coefficients, matrixing the pulse wave characteristic points to determine a pulse wave characteristic matrix.
The working principle and beneficial effects of the technical scheme are as follows:
The technical scheme provides an embodiment, wherein the transmitting the maximum attenuation data to a preset chebyshev filter and establishing a green light filtering mechanism comprise the following steps: collecting green light pulse wave signals through the Chebyshev filter, and determining green light pulse wave characteristic points of the green light pulse wave signals; fitting the pulse wave feature points, determining feature coefficients of the green light pulse feature points, and matrixing the pulse wave feature points according to the feature coefficients to determine a pulse wave feature matrix; transmitting the maximum attenuation data to a preset chebyshev filter, filtering the pulse wave characteristic matrix, and determining a target filtering matrix; and according to the target filter matrix, a green light filter mechanism is established, and the whole process adapts to the body environment of the object to be detected, so that the detection precision of the object to be detected is improved.
Example 7:
The present technical solution provides an embodiment, wherein the establishing a green light filtering mechanism according to the target filtering matrix includes:
Step 1: acquiring a preset ideal filter matrix
Wherein, density represents ideal filter matrix, n×n represents matrix of n rows and n columns of ideal filter matrix, density represents pulse wave characteristic data under ideal filter condition in ideal filter matrix;
step 2: obtaining a target filter matrix
Wherein, target represents the target filter matrix, n×n represents the matrix of n rows and n columns of the target filter matrix, and target represents the pulse wave characteristic data under the condition of target filtering in the target filter matrix;
step 3: determining a transformation coefficient matrix according to the target filter matrix and the ideal filter matrix;
wherein W n×n represents a transformation coefficient matrix, and E n×n represents an identity matrix;
step 4: establishing a green light filtering spectrum matrix through a transformation coefficient matrix;
wherein r represents a green light filtering spectrum matrix;
Step 5: and determining a green light filtering mechanism according to the green light filtering spectrum matrix.
The working principle of the technical scheme is as follows:
The technical scheme provides an embodiment, wherein preset ideal filter matrix Density n×n is obtained, pulse wave characteristic data Density under the condition of ideal filtering is contained in the ideal filter matrix, target filter matrix Target is obtained, and pulse wave characteristic data Target under the condition of Target filtering is contained in the Target filter matrix; according to the target filter matrix and the ideal filter matrix, a transformation coefficient matrix W n×n is determined, which is to filter the ideal parameters of the ideal filter matrix through the target filter matrix, and a coefficient correlation matrix between a green filter spectrum matrix r and the transformation coefficient matrix W n×n is determined, and according to the green filter spectrum matrix, a green filter mechanism is determined, so that according to the target filter matrix, a green filter mechanism is established.
The beneficial effects of the technical scheme are as follows:
according to the technical scheme, the acquired coefficient of the target filter matrix in an ideal state is converted, so that the conversion transformation space matrix of the target filter matrix is determined, the flexibility and the robustness of pulse characteristic point conversion are greatly expanded, and the fault tolerance and the adaptability of pulse characteristic points are improved.
Example 8:
the present disclosure provides an embodiment, where the searching for a red light feature point according to the red light pulse wave signal, and determining a first signal component based on the red light feature point includes:
Calculating the intensity of the red pulse wave according to the red pulse wave signal;
intensity sorting is carried out on the red light pulse wave intensities, and a first intensity peak value is determined;
collecting and reconstructing red light characteristic points through the first intensity peak value;
According to the red light characteristic points, a red light direct current component and a red light alternating current component are calculated; wherein,
The red light alternating current component is used for obtaining a change signal of arterial pulse based on photoplethysmography under the action of red light;
And determining a first signal component according to the red direct current component and the red alternating current component.
The technical scheme has the beneficial effects that:
According to the technical scheme, a red light characteristic point is searched according to the red light pulse wave signal, a first signal component is determined based on the red light characteristic point, the first signal component is composed of a red light alternating current component and a red light direct current component, the characteristic point is regular rhythms of pulse operation of a user under the irradiation of red light, wave peaks and wave trough values of the regular rhythms are collected, so that characteristic points are formed, according to the red light pulse wave signal, the red light pulse wave intensity is calculated and is similar to the infrared light pulse wave intensity, the intensity of the red light pulse wave is ordered, and a first intensity peak value is determined; collecting and reconstructing red light characteristic points through the first intensity peak value, wherein the red light characteristic points have complex and changeable rhythms and have a plurality of noise or other interference factors, so that infrared light or red light needs to be split, direct current refers to a constant logistics signal, alternating current refers to a signal with a constant direction, and a red light direct current component and a red light alternating current component are calculated according to the red light external characteristic points; wherein the red light alternating current component is a change signal of arterial pulse obtained based on photoplethysmography under the action of the infrared light; according to the red light direct current component and the red light alternating current component, a first signal component is determined, and the whole embodiment analyzes pulse waves under irradiation of infrared light and red light, so that collected coarse signals are slowly stripped, the most accurate characteristic points are obtained, and the method has excellent robustness and anti-interference capability.
Example 9:
the present technical solution provides an embodiment, according to the infrared pulse wave signal, searching an infrared light feature point, and determining a second signal component based on the infrared light feature point, including:
Calculating the infrared light pulse wave intensity according to the infrared light pulse wave signal;
intensity sorting is carried out on the infrared light pulse wave intensities, and a second intensity peak value is determined;
collecting and reconstructing infrared light characteristic points through the second intensity peak value;
According to the infrared light external characteristic points, an infrared light direct current component and an infrared light alternating current component are calculated; wherein,
The infrared light alternating current component is a change signal of arterial pulse obtained based on photoplethysmography under the action of the infrared light;
and determining a second signal component according to the infrared light direct current component and the infrared light alternating current component.
The working principle and beneficial effects of the technical scheme are as follows:
according to the infrared pulse wave signal, searching an infrared light characteristic point, determining a second signal component based on the infrared light characteristic point, wherein the second signal component consists of an infrared light alternating current component and an infrared light direct current component, the characteristic point is regular rhythms of pulse operation of a user under the irradiation of infrared light, peaks and troughs of the regular rhythms are collected, so that characteristic points are formed, according to the infrared light pulse wave signal, the infrared light pulse wave intensity is calculated and is similar to the red light pulse wave intensity, the infrared light pulse wave intensity is sequenced, and a second intensity peak value is determined; collecting and reconstructing infrared light characteristic points through the second intensity peak value, wherein the infrared light or red light is required to be split because of complex and changeable rhythms of the infrared light characteristic points and a plurality of noise or other interference factors, direct current refers to a constant logistics signal, alternating current refers to a signal with a constant direction, and an infrared light direct current component and an infrared light alternating current component are calculated according to the infrared light external characteristic points; wherein the infrared light alternating current component is a change signal of arterial pulse obtained based on photoplethysmography under the action of the infrared light; according to the infrared light direct current component and the infrared light alternating current component, a second signal component is determined, and the whole embodiment analyzes pulse waves under the irradiation of infrared light and red light, so that collected coarse signals are slowly stripped, the most accurate characteristic points are obtained, and the method has excellent robustness and anti-interference capability.
Example 10:
The present technical solution provides an embodiment, where the filtering processing is performed on the first signal component and the second signal component by using a green light filtering mechanism, and a dynamic adaptive model is built after the processing, and the filtering processing is performed on the first signal component and the second signal component by using a green light filtering mechanism, and the dynamic adaptive model is built after the processing, where the method includes:
Step S1, collecting a first signal component delta sig 1(fd1,fs1) and a second signal component delta sig 2(fd2,fs2);
Wherein Δsig 1 represents the first signal component, f d1 represents the first dc component of the first signal component, f s1 represents the first ac component of the first signal component, Δsig 2 represents the second signal component, f d2 represents the second dc component of the second signal component, and f s2 represents the second ac component of the second signal component;
Step S2: acquiring a green light filtering spectrum matrix r of a green light filtering mechanism, performing cross spectrum calculation on the first signal component delta sig 1(fd1,fs1) and the second signal component delta sig 2(fd2,fs2), and determining a cross signal;
Wherein S n (sig) represents a cross signal, E represents an identity matrix which is multiplied by a green light filtering spectrum matrix, i represents ordinal numbers of an ith signal component collected in the first signal component and the second signal component, n represents a first signal component and the second signal component collected together, and i epsilon (1, n);
step S3: transmitting the cross signal to the green light filtering mechanism to obtain a dynamic self-adaptive model function;
wherein, the fer dynamic self-adaptive model function, T represents all the acquired signal periods, T represents one complete signal period in the complete signal period, Representing a dynamic self-adaptive function, wherein θ represents the period offset of different signals based on the dynamic state under the self-adaptive condition, and q represents the number of the period offsets of different signals acquired;
Step S4: when the fer dynamic self-adaptive model function cannot be synthesized, the wearable device automatically reports errors;
step S5: and when the fer dynamic self-adaptive model function is synthesized, establishing a dynamic self-adaptive model through the dynamic self-adaptive model function.
The working principle of the technical scheme is as follows:
According to the technical scheme, the first signal component and the second signal component are collected and stored, a dynamic self-adaptive model is built based on the green light filtering mechanism, and the blood oxygen saturation of an object to be detected is determined based on the dynamic self-adaptive model, and the method comprises the following steps: collecting a first signal component Δsig 1(fd1,fs1) and a second signal component Δsig 2(fd2,fs2); acquiring a green light filtering spectrum matrix r of a green light filtering mechanism, performing cross spectrum calculation on the first signal component delta sig 1(fd1,fs1) and the second signal component delta sig 2(fd2,fs2), and determining a cross signal; s n (sig) transmits the crossing signal to the green light filtering mechanism to obtain a dynamic and dynamic self-adaptive model function; when the fer dynamic self-adaptive model function cannot be synthesized, the wearable device automatically reports errors; when a fer dynamic self-adaptive model function is synthesized, a dynamic self-adaptive model is built through the dynamic self-adaptive model function; and acquiring and processing physical sign data of the object to be detected based on the dynamic self-adaptive model, and determining the blood oxygen saturation of the object to be detected.
The beneficial effects of the technical scheme are as follows:
According to the technical scheme, the self-adaptive model is built, the multi-shunt signals are continuously processed, the impurity is removed, the self-adaptive learning model is built, the requirements of objects to be detected in different states of different scenes are met flexibly, the physical condition of the objects to be detected is monitored in real time, and the self-adaptive learning model has excellent robustness and anti-interference capability.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. A three-wavelength blood oxygen saturation monitoring method based on a wearable device, comprising the steps of:
acquiring pulse wave signals of an object to be detected; wherein,
The pulse wave signals comprise a green light pulse wave signal, a red light pulse wave signal and an infrared light pulse wave signal;
acquiring a state filtering range of a green light pulse wave signal based on the environment of an object to be detected, and generating a green light filtering mechanism according to the state filtering range;
Searching a red light characteristic point according to the red light pulse wave signal, and determining a first signal component based on the red light characteristic point;
searching infrared light characteristic points according to the infrared light pulse wave signals, and determining a second signal component based on the infrared light characteristic points;
Filtering the first signal component and the second signal component through a green light filtering mechanism, and establishing a dynamic self-adaptive model after the processing;
calculating the blood oxygen saturation of the object to be detected based on the dynamic self-adaptive model;
The method for generating the green light filtering mechanism comprises the steps of obtaining a state filtering range of a green light pulse wave signal based on the environment of an object to be detected, and generating the green light filtering mechanism according to the state filtering range, wherein the method comprises the following steps:
Acquiring green light pulse wave signals of an object to be detected at fixed time, and determining characteristic frequencies of the green light pulse wave signals according to the green light pulse wave signals;
acquiring signal influence parameters of pulse wave signals according to the characteristic frequency; wherein,
The signal influence parameters comprise a fluctuation coefficient, a cut-off frequency and a low-pass impedance order of the pulse wave signal;
Calculating maximum attenuation data according to the signal influence parameters; wherein,
The maximum attenuation data are loss data based on the influence of self parameters of the wearable equipment;
And transmitting the maximum attenuation data to a preset chebyshev filter based on the environment of the object to be detected, and establishing a green light filtering mechanism.
2. The method for monitoring three-wavelength blood oxygen saturation based on a wearable device according to claim 1, wherein before the step of acquiring the pulse wave signal of the object to be detected, the method further comprises:
Based on a preset sensing device in the wearable equipment, judging whether an object to be detected is in a static state or not:
When detecting that the object to be detected is in a static state, automatically issuing an acquisition instruction based on preset wearing equipment, and acquiring a red light pulse wave signal and an infrared light pulse signal of the object to be detected;
when detecting that the object to be detected is not in a static state, automatically issuing an acquisition instruction based on preset wearing equipment, and acquiring a green light pulse wave signal of the object to be detected.
3. The method for monitoring three-wavelength blood oxygen saturation based on a wearable device according to claim 1, wherein the step of acquiring the pulse wave signal when the object to be detected is in a stationary state comprises the steps of:
based on a preset wearable device, alternately emitting light waves to an object to be detected in a static state at regular time; wherein,
The light wave rays comprise green light rays, red light rays and infrared light rays;
acquiring the luminous intensity of the red light ray and the infrared light ray, and determining the arterial pulse optical path of an object to be detected according to the luminous intensity;
Recording the pulse intensity of the object to be detected in real time according to the arterial pulse optical path;
and determining a pulse wave signal of the object to be detected according to the pulse intensity.
4. The method for monitoring three-wavelength blood oxygen saturation based on a wearable device as claimed in claim 1, wherein the transmitting the maximum attenuation data to a preset chebyshev filter and establishing a green light filtering mechanism based on the environment of the object to be detected comprises:
based on the environment of an object to be detected, acquiring green light filtering frequency through a preset chebyshev filter; wherein,
The filtering frequency is noise frequency and abnormal signals of the object to be detected in the environment;
Calculating a pulse wave characteristic matrix of the object to be detected in a non-stationary state;
Acquiring and transmitting maximum attenuation data to a chebyshev filter, filtering the pulse wave feature matrix through green light filtering frequency, and determining a target filtering matrix;
and establishing a green light filtering mechanism according to the target filtering matrix.
5. The method for monitoring three-wavelength blood oxygen saturation based on a wearable device according to claim 4, wherein the calculating the pulse wave feature matrix of the object to be detected in the non-stationary state comprises:
When the object to be detected is in a non-stationary state, collecting green light pulse wave signals of the object to be detected;
According to the green light pulse wave signals, determining green light pulse wave characteristic points;
Fitting and calculating the pulse wave feature points, and determining feature coefficients of green light pulse feature points;
And according to the characteristic coefficients, matrixing the pulse wave characteristic points to determine a pulse wave characteristic matrix.
6. The method for monitoring three-wavelength blood oxygen saturation based on a wearable device according to claim 4, wherein the establishing a green light filtering mechanism according to the target filtering matrix comprises:
Step 1: acquiring a preset ideal filter matrix
Wherein, density represents ideal filter matrix, n×n represents matrix of n rows and n columns of ideal filter matrix, density represents pulse wave characteristic data under ideal filter condition in ideal filter matrix;
step 2: obtaining a target filter matrix
Wherein, target represents the target filter matrix, n×n represents the matrix of n rows and n columns of the target filter matrix, and target represents the pulse wave characteristic data under the condition of target filtering in the target filter matrix;
step 3: determining a transformation coefficient matrix according to the target filter matrix and the ideal filter matrix;
wherein W n×n represents a transformation coefficient matrix, and E n×n represents an identity matrix;
step 4: establishing a green light filtering spectrum matrix through a transformation coefficient matrix;
wherein r represents a green light filtering spectrum matrix;
Step 5: and determining a green light filtering mechanism according to the green light filtering spectrum matrix.
7. The method for monitoring three-wavelength blood oxygen saturation based on a wearable device according to claim 1, wherein the searching for a red characteristic point according to the red pulse wave signal and determining a first signal component based on the red characteristic point comprises:
Calculating the intensity of the red pulse wave according to the red pulse wave signal;
intensity sorting is carried out on the red light pulse wave intensities, and a first intensity peak value is determined;
collecting and reconstructing red light characteristic points through the first intensity peak value;
According to the red light characteristic points, a red light direct current component and a red light alternating current component are calculated; wherein,
The red light alternating current component is used for obtaining a change signal of arterial pulse based on photoplethysmography under the action of red light;
And determining a first signal component according to the red direct current component and the red alternating current component.
8. The method for monitoring three-wavelength blood oxygen saturation based on a wearable device according to claim 1, wherein the searching for an infrared light feature point according to the infrared light pulse wave signal and determining a second signal component based on the infrared light feature point comprises:
Calculating the infrared light pulse wave intensity according to the infrared light pulse wave signal;
intensity sorting is carried out on the infrared light pulse wave intensities, and a second intensity peak value is determined;
collecting and reconstructing infrared light characteristic points through the second intensity peak value;
According to the infrared light external characteristic points, an infrared light direct current component and an infrared light alternating current component are calculated; wherein,
The infrared light alternating current component is a change signal of arterial pulse obtained based on photoplethysmography under the action of the infrared light;
and determining a second signal component according to the infrared light direct current component and the infrared light alternating current component.
9. The method for monitoring three-wavelength blood oxygen saturation based on a wearable device according to claim 1, wherein the filtering of the first signal component and the second signal component by a green light filtering mechanism and establishing a dynamic adaptive model after the processing, comprises:
step S1, collecting a first signal component delta sig 1(fd1,fs1) and a second signal component delta sig 2(fd2,fs2);
Wherein Δsig 1 represents the first signal component, f d1 represents the first dc component of the first signal component, f s1 represents the first ac component of the first signal component, Δsig 2 represents the second signal component, f d2 represents the second dc component of the second signal component, and f s2 represents the second ac component of the second signal component;
step S2: acquiring a green light filtering spectrum matrix r of a green light filtering mechanism, performing cross spectrum calculation on the first signal component delta sig 1(fd1,fs1) and the second signal component delta sig 2(fd2,fs2), and determining a cross signal;
Wherein S n (sig) represents a cross signal, E represents an identity matrix which is multiplied by a green light filtering spectrum matrix, i represents ordinal numbers of an ith signal component collected in the first signal component and the second signal component, n represents a first signal component and the second signal component collected together, and i epsilon (1, n);
step S3: transmitting the cross signal to the green light filtering mechanism to obtain a dynamic self-adaptive model function;
wherein, the fer dynamic self-adaptive model function, T represents all the acquired signal periods, T represents one complete signal period in the complete signal period, Representing a dynamic self-adaptive function, wherein θ represents the period offset of different signals based on the dynamic state under the self-adaptive condition, and q represents the number of the period offsets of different signals acquired;
Step S4: when the fer dynamic self-adaptive model function cannot be synthesized, the wearable device automatically reports errors;
step S5: and when the fer dynamic self-adaptive model function is synthesized, establishing a dynamic self-adaptive model through the dynamic self-adaptive model function.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110428234.6A CN113274008B (en) | 2021-04-21 | 2021-04-21 | A three-wavelength blood oxygen saturation monitoring method based on wearable devices |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110428234.6A CN113274008B (en) | 2021-04-21 | 2021-04-21 | A three-wavelength blood oxygen saturation monitoring method based on wearable devices |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113274008A CN113274008A (en) | 2021-08-20 |
| CN113274008B true CN113274008B (en) | 2024-11-08 |
Family
ID=77276989
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110428234.6A Active CN113274008B (en) | 2021-04-21 | 2021-04-21 | A three-wavelength blood oxygen saturation monitoring method based on wearable devices |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113274008B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113545777B (en) * | 2021-08-31 | 2024-05-24 | 维沃移动通信有限公司 | Blood oxygen detection method, blood oxygen detection device, electronic device and readable storage medium |
| CN118633932A (en) * | 2024-05-14 | 2024-09-13 | 南方科技大学 | A blood oxygen monitoring method and device based on multi-band fusion noise reduction |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10568525B1 (en) * | 2015-12-14 | 2020-02-25 | Fitbit, Inc. | Multi-wavelength pulse oximetry |
| CN111685726A (en) * | 2019-03-12 | 2020-09-22 | 华为终端有限公司 | Blood oxygen detection method and device |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102499694B (en) * | 2011-09-22 | 2013-11-27 | 中国人民解放军第三军医大学野战外科研究所 | Method for eliminating interference to blood oxygen saturation monitoring |
| US10687742B2 (en) * | 2015-06-12 | 2020-06-23 | ChroniSense Medical Ltd. | Using invariant factors for pulse oximetry |
| CA3045025A1 (en) * | 2016-12-15 | 2018-06-21 | Rhythm Diagnostic Systems, Inc. | Health monitoring systems and methods |
| CN109044296A (en) * | 2018-09-03 | 2018-12-21 | 深圳市尼欧科技有限公司 | A kind of driving fatigue prompting bracelet of intelligence physiological fatigue degree detection |
-
2021
- 2021-04-21 CN CN202110428234.6A patent/CN113274008B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10568525B1 (en) * | 2015-12-14 | 2020-02-25 | Fitbit, Inc. | Multi-wavelength pulse oximetry |
| CN111685726A (en) * | 2019-03-12 | 2020-09-22 | 华为终端有限公司 | Blood oxygen detection method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113274008A (en) | 2021-08-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11259753B2 (en) | Systems and methods for detecting photoplethysmographic device usage | |
| Schäck et al. | Computationally efficient heart rate estimation during physical exercise using photoplethysmographic signals | |
| CN109414203A (en) | Online heart rate estimation based on optical measurement | |
| CN105496416A (en) | Human motion state recognition method and device | |
| CN108937957B (en) | Detection method, device and detection equipment | |
| CN114052693B (en) | Heart rate analysis method, device and equipment | |
| CA2250331A1 (en) | Method and apparatus for removing artifact from physiological signals | |
| Kumar et al. | STSR: Spectro-temporal super-resolution analysis of a reference signal less photoplethysmogram for heart rate estimation during physical activity | |
| CN115191961B (en) | Cardiopulmonary health detection method and device, wearable equipment and storage medium | |
| CN106137219B (en) | The absolute difference adduction of dual wavelength calculates arterial oxygen saturation method and device thereof | |
| CN113274008B (en) | A three-wavelength blood oxygen saturation monitoring method based on wearable devices | |
| CN116058814B (en) | Heart rate detection method and electronic device | |
| CN119097310A (en) | A health monitoring bracelet detection method and system capable of real-time detection of pulse rate and blood oxygen | |
| US11000234B2 (en) | Wearable device, method and apparatus for eliminating motion interference | |
| CN105816165A (en) | Real-time dynamic heart rate monitoring device and monitoring method | |
| CN117715583A (en) | Estimating oxygen saturation using green visible light as a filter | |
| CN116327188B (en) | Blood oxygen analysis method based on finger-clip type oximeter | |
| CN117653035A (en) | Motion artifact removing method and system based on differential PPG signals | |
| CN120323969B (en) | A blood oxygen detection calculation method based on infrared light and visible light | |
| CN115363543B (en) | Uric acid monitoring method based on wearable equipment | |
| CN106419884B (en) | A kind of rate calculation method and system based on wavelet analysis | |
| TWI846293B (en) | Apparatuses for detecting heartbeat and methods for detecting heartbeat | |
| CN209932708U (en) | Array arrangement circuit for measuring heart rate and blood oxygen | |
| CN106667474A (en) | Heart rate measuring method and device suitable for intelligent wearable device | |
| JP3763480B2 (en) | Method and apparatus for measuring pulse rate and saturation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |