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WO2018140429A1 - Procédé, système et dispositif d'analyse de cinématique d'articulation de cheville - Google Patents

Procédé, système et dispositif d'analyse de cinématique d'articulation de cheville Download PDF

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Publication number
WO2018140429A1
WO2018140429A1 PCT/US2018/014944 US2018014944W WO2018140429A1 WO 2018140429 A1 WO2018140429 A1 WO 2018140429A1 US 2018014944 W US2018014944 W US 2018014944W WO 2018140429 A1 WO2018140429 A1 WO 2018140429A1
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WIPO (PCT)
Prior art keywords
sensor
flexible panel
sensors
data
emg
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.)
Ceased
Application number
PCT/US2018/014944
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English (en)
Inventor
Alexander BINA
Hobey Tam
Zachary T. REINHARDT
Tanya COLONNA
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Blacktop Labs LLC
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Blacktop Labs LLC
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Publication of WO2018140429A1 publication Critical patent/WO2018140429A1/fr
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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4528—Joints
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/112—Gait analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4538—Evaluating a particular part of the muscoloskeletal system or a particular medical condition
    • A61B5/4585—Evaluating the knee
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4538—Evaluating a particular part of the muscoloskeletal system or a particular medical condition
    • A61B5/4595—Evaluating the ankle

Definitions

  • This invention relates generally to the art of sensors and the use of sensors for monitoring muscle activation patterns and joint motility.
  • Neuromuscular degenerative diseases affect how motor neurons communicate with muscle tissue to control motion required for daily routines such as walking.
  • These neuromuscular degenerative conditions include, but are not limited to, cerebral palsy, stroke, traumatic brain injury, spinal cord injury and Parkinson's disease. Individuals suffering from these conditions require physical therapy to slow degeneration and to rehabilitate their ability to control motion required for normal daily activities and to maintain their independence.
  • Parkinson's disease is the most common neurodegenerative disease; it affects approximately 1 million people in the United States over the age of 50 with 60,000 new cases diagnosed annually. People with Parkinson's disease have spatio-temporal gait abnormalities that range in severity, depending on individual physiology and what stage of Parkinson's they are experiencing. The most common characteristics of Parkinson's gait include: difficulty turning or changing direction, festination, foot drag, freezing of gait, shuffling gait, start-and- stop hesitation, and abnormal muscle activation patterns. These gait variations increase their risk of falling and decrease their ability to perform daily activities without assistance.
  • gait labs traditionally consist of motion capture devices and motion analysis software. These are expensive in nature because of the equipment (upwards of $ 1 ,500 for hardware and image processing software) and space needed for accurate data collection thus requiring high overhead costs for clinics.
  • Oxford metrics for the new 9 camera Vicon System suggests a minimum space of 6 meters by 12 meters for data collection, require a relatively long set-up time, and put restrictions on the metrics the clinician is able to collect.
  • laboratory conditions do not accurately model the patient in their regular environment. It has been demonstrated in the literature that for valid and reliable assessment of gait parameters, gait should be performed over ample walking distances.
  • Parkinson's disease patients can monitor their progress with metrics directly associated with the two main goals of physical therapy: to decrease a patient -s fall risk and to increase a patient's independence.
  • the output metrics are currently specifically targeted to Parkinson's disease rehabilitation
  • the senor integration system is scalable to output metrics associated with rehabilitation of additional neuromuscular degenerative conditions as well as orthopedic injuries and muscular activation abnormalities.
  • Figure 1 A illustrates a front view of an example mechanical embodiment of the system and device of this invention.
  • Figure IB illustrates a back view of an example mechanical embodiment of the system and device of this invention.
  • Figure 1C illustrates a side lateral view of an example mechanical embodiment of the system and device of this invention.
  • Figure ID illustrates a side medial view of an example mechanical embodiment of the system and device of this invention.
  • Figure IE illustrates a bottom view of an example mechanical embodiment of the system and device of this invention.
  • Figure I F illustrates example HEDs and PEDs.
  • Figure 2 illustrates the electronic hardware architecture of the system in accordance with this invention. Black arrows indicate flow of data. Power supply provides power to all components necessitating electrical power for operation.
  • Figure 3 illustrates a block diagram illustration the sequence of the method of analyzing the raw data in accordance with this invention.
  • Figure 4 is a block diagram illustrating example onboard raw biometric conditioning techniques to be utilized for conditioning circuits for respective sensors in accordance with this invention.
  • Figure 5 A illustrates a front view of an example EMG sensor array in accordance with this invention.
  • Figure 5B illustrates a back view of an example EMG sensor array in accordance with this invention.
  • Figure 5C illustrates a side lateral view of an example EMG sensor array in accordance with this invention.
  • Figure 6 illustrates a block diagram to relay certain data from any plurality of EMG sensors to the mother board for further conditioning
  • Figure 7 A illustrates an example of raw biometric correlation for EMG sensor array for an anterior tibialis muscle throughout an arbitrary gait cycle of a human.
  • Figure 7B illustrates an example of raw biometric correlation for EMG sensor array for an gastrocnemius muscle throughout an arbitrary gait cycle of a human.
  • Figure 7C illustrates an example of raw biometric correlation for EMG sensor array for an solcus muscle throughout an arbitrary gait cycle of a human.
  • Figure 8A illustrates an example front isometric view of the ankle sensor array.
  • Figure 8B illustrates an example backside, inferior isometric view of the ankle sensor array.
  • Figure 9 A illustrates a detailed example ankle bend sensor array
  • figure 9B illustrates an example motherboard and power supply housing
  • Figure 9C illustrates an example block diagram of the ankle bend sensor array
  • Figure 10A illustrates a detailed example heel sensor array
  • Figure 10B illustrates an example motherboard and power supply housing.
  • Figure IOC illustrates an example block diagram of the heel sensor array.
  • Figure 1 1 A illustrates an example top, front forefoot sensor array.
  • Figure 1 IB illustrates an example anterior, front forefoot sensor array.
  • Figure 11C illustrates an example anterior forefoot sensor array.
  • Figure 1 I D illustrates an example block diagram of the forefoot sensor array.
  • Figure 12A illustrates an example of raw biometric correlation FSR sensor array of a heel throughout an arbitrary gait cycle of a human.
  • Figure 12B illustrates an example of raw biometric correlation forefoot FSR sensor array throughout an arbitrary gait cycle of a human.
  • Figure 13 illustrates an example of correlation interpretation of a raw biometric correlation FSR sensor array of a heel and forefoot throughout an arbitrary gait cycle of a human.
  • Figure 14A illustrates an example of raw biometric correlation IMU sensor array throughout an arbitrary gait cycle of a human.
  • Figure 14B illustrates an example of raw biometric correlation flex sensor array throughout an arbitrary gait cycle of a human.
  • Figure IS A illustrates an arbitrary gait cyele of a human.
  • Figure 15B illustrates an example data processing method.
  • Figure 16 is example analyzed EMG data to determine co-contraction during an arbitrary gait cycle.
  • Figure 17 is example analyzed IMU data to model velocity and displacement profile in multiple directions during an arbitrary gait cycle.
  • This invention provides a method, system, and device for gathering, analyzing, and communicating kinematic, kinetic, and muscle activation data representing joint behavior via novel sensor arrays, signal processing, and data analysis.
  • a covering adapted to be worn along a flexible joint comprising: a flexible panel adapted for being worn along at least one of a joint of an ankle, a knee, an elbow, or a wrist, the flexible panel being engaged by a plurality of sensors including at least one flex sensor positioned in operative engagement with the flexible panel, the flex sensor further being positioned within a pivot area of the joint when the covering is worn; at least one EMG sensor in operative engagement with the flexible panel; at least one IMU sensor in operative engagement with the flexible panel, the IMU sensor selected from the groups consisting of an accelerometer, gyrometer, magnetometer and combinations thereof; optionally providing a FSR sensor that is in operative communication with the flexible panel and is positioned along a side of the flexible joint; wherein, data collected by one or more of the plurality of sensors when the flexible panel is worn and responds to a user's movement, is transmitted to a CPU for analysis.
  • the covering may extend from a lower leg to a bottom portion of a user's foot wherein a portion of the covering opposite a bottom of a user's foot provides for at least one sensor selected from the group of sensors consisting of a FSR sensor, a flex sensor, a stretch sensor, a piezoelectric sensor, an accelerometer, an EMG ground reference electrode, and combinations thereof.
  • the covering may further include a forefoot bottom sensor selected from the group consisting of a FSR sensor, a flex sensor, a stretch sensor, a piezoelectric sensor, an accelerometer, a magnetometer and combinations thereof.
  • a forefoot bottom sensor selected from the group consisting of a FSR sensor, a flex sensor, a stretch sensor, a piezoelectric sensor, an accelerometer, a magnetometer and combinations thereof.
  • the covering may further define a forefoot top sensor selected from the group consisting of a FSR sensor, a flex sensor, a stretch sensor, a piezoelectric sensor,
  • the covering may further define at least one sensor selected from the group of sensors consisting of an accelerometer, an EMG sensor, and combinations thereof.
  • the flexible panel may be selected from a group consisting of fabrics, flexible polymers, conductive fabrics, elastomers, woven fabrics, non-woven fabrics, bandages, fabric wraps, socks, foot wear, and combinations thereof.
  • It is a further aspect of at least one embodiment of the present invention to provide a system for measuring joint motility and muscle activation data comprising: a flexible panel adapted for being worn along at least one of an ankle joint, a knee, an elbow, or a wrist; at least one flex sensor positioned in operative engagement with the flexible panel, the flex sensor further being positioned within a pivot area of the joint when the flexible panel covering is worn; optionally providing a FSR sensor that is in operative communication with the flexible panel and is positioned along a side of the flexible joint; a module for transferring data from one or more sensors within the flexible panel to the CPU; wherein, data collected by the sensors when the flexible panel is worn and responds to a user's movement, is transmitted to a CPU; wherein data from sensors within the flexible panel is analyzed by the CPU and displayed on a PED.
  • the system may include a flexible panel that is worn along an ankle and which extends along a lower length to a bottom portion of a user's foot.
  • a portion of the flexible panel opposite a bottom of a user's foot provides for at least one sensor selected from the group of sensors consisting of a FSR sensor, a flex sensor, a stretch sensor, a piezoelectric sensor, an accelerometer, an EMG ground reference electrode, and a magnometer combinations thereof.
  • a portion of the flexible panel may include a forefoot bottom sensor selected from the group consisting of a FSR sensor, a flex sensor, a stretch sensor, a piezoelectric sensor, an accelerometer, and combinations thereof.
  • the flexible panel may further provide a forefoot top sensor selected from the group consisting of a FSR sensor, a flex sensor, a stretch sensor, a piezoelectric sensor, an accelerometer, and combinations thereof, and when the flexible panel is opposite a calf region of a user, the panel further defines at least one sensor selected from the group of sensors consisting of an accelerometer, an EMG sensor, and combinations thereof.
  • a forefoot top sensor selected from the group consisting of a FSR sensor, a flex sensor, a stretch sensor, a piezoelectric sensor, an accelerometer, and combinations thereof
  • It is a further aspect of at least one embodiment of the present invention to provide a process of measuring functionality of a human joint comprising the steps of: providing a flexible panel adapted for being worn along at least one of an ankle joint, a knee, an elbow, or a wrist, the flexible panel being engaged by a plurality of sensors including at least one flex sensor positioned in operative engagement with the flexible panel, the flex sensor further being positioned within a pivot area of the flexible joint when the flexible panel is worn; and optionally providing a FSR sensor that is in operative communication with the flexible panel and is positioned along a side of the joint, and wherein, data is collected by the sensors when the flexible panel is worn and responds to a user's movement; moving the joint having the flexible panel positioned thereto along a pivot point for a series of repetitive motions;
  • the process may include the use of a flexible panel worn along an ankle and which extends along a lower length to a bottom portion of a user's foot and where the portion of the flexible panel opposite a bottom of a user' s foot provides for at least one sensor selected from the group of sensors consisting of a FSR sensor, a flex sensor, a stretch sensor, a piezoelectric sensor, an accelerometer, an EMG ground reference electrode, and combinations thereof.
  • the process may include use of a portion of the flexible panel that further includes a forefoot bottom sensor selected from the group consisting of a FSR sensor, a flex Sensor, a stretch sensor, a piezoelectric sensor, an accelerometer, and combinations thereof, and wherein process further provides for the use of a forefoot top sensor selected from the group consisting of a FSR sensor, a flex sensor, a stretch sensor, a piezoelectric sensor, an accelerometer, a magnometer and combinations thereof.
  • a forefoot bottom sensor selected from the group consisting of a FSR sensor, a flex Sensor, a stretch sensor, a piezoelectric sensor, an accelerometer, a magnometer and combinations thereof
  • a forefoot top sensor selected from the group consisting of a FSR sensor, a flex sensor, a stretch sensor, a piezoelectric sensor, an accelerometer, a magnometer and combinations thereof.
  • the portion of the flexible panel further defines at least one sensor selected from the group of sensors consisting of an accelerometer, an EMG sensor, and combinations thereof.
  • US Patent No. 8,963,538, which is incorporated herein by reference, provides for a magnetometer test arrangement and method in which various forms, types and usage of a multi-axis magnetometer is disclosed.
  • US Patent Publication No. US2016/0317058 which is incorporated herein by reference, is directed towards an electromyography sensor that can be used with one or more embodiments of the present invention.
  • US Patent No. 9,089,273 which is incorporated herein by reference, is directed to various electrodes placed in a textile environment such that the fabric is conductive. Such a fabric can be useful with EMG sensor and electrodes.
  • US Patent Publication No; US2016/0Q58519 which is incorporated herein by reference, describes an apparatus and method for fabrication of a mechanical interface between a wearable device in a human body segment and sets forth an ability to provide for EMG electrodes that can be manufactured using a 3D printing process and which is also useful for EEG electrodes.
  • FSR - force sensiti ve resister type of sensor used to detect force over a certain area geometrically constrained by the sensor (pressure)
  • an IMU is a sensor system consisting of an accelerometer, magnetometer, or gyrometer or any combination thereof.
  • Biometric data certain data originating from the human body depicting some type of physical phenomena manifesting as a voltage output representing the biosignal originating, occurring within, or about the human body
  • Biometric data correlation (graph) - analyzed biometric data that may be graphically represented on GUI; examples include the figures
  • Clinical metric certain outputs from the software analysis program that trigger clinicians to take clinical action
  • Hardware device - preferred embodiment of all electrical and housing components to enable data acquisition and data transfer of biometric data pertaining to lower limb movement of a person. Includes mother board, EMG sensor array, ankle sensor array, and forefoot array.
  • Electrical components includes circuit components, sensors, microprocessor, and any other modules necessary to acquire and transfer biometric data pertaining to lower limb movement of a person.
  • Motherboard includes all electrical components except for sensors and wires necessary to relay raw biometric data from sensors to rest of electrical components.
  • Housing - includes arbitrary flexible plastic containers for sensor arrays and arbitrary elastic fabric arbitrary flexible plastic containers are inserted, affixed, or fastened or any combination thereof to.
  • Conditioning circuit a certain collection of ⁇ electrical, components such as resisters, capacitors, etc. necessary to physically manipulate raw data such that the signal is cleaned enough for further interpretation. Each conditioning circuit is different given the sensor signal it is designed to clean.
  • Sensor array - a specific collection of sensors geometrically oriented to obtain certain data pertinent to analysis
  • EMG sensor array - one s two, or three or any plurality of EMG sensors geometrically oriented to specifically obtain EMG data on the lower or upper leg of a person.
  • Muscles include but are not limited to gastrocnemius, anterior tibialis, soleus, gluteus medial, gluteus dorsa, quadriceps, etc. or any combination thereof.
  • Ankle sensor array preferred embodiment of the ankle bend sensor array with the heel cup sensor array inserted into an arbitrary textile, elastic housing capable of stretching to accommodate any plurality of arbitrary body types.
  • Ankle bend sensor array - preferred embodiment of a flex sensor combined with an IMU affixed, inserted, or fastened into an arbitrary flexible polymer housing of a certain geometry that allows biometric data to be taken pertaining to the motion and kinematics at the bend of the ankle of a person.
  • Bottom forefoot sensor array - preferred embodiment of one, two, or any plurality of FSRs affixed, inserted, or fastened into an arbitrary flexible polymer housing in a certain geometry and orientation that allows detection of ground reaction forces of the forefoot.
  • PDF - portable document file PED - personal electronic device these include PCs, macs, tablets, ipads, smart phones or any device capable of wireless or cellular connectivity with processing capabilities.
  • HED - hospital electronic device these devices are securely integrated into the hospital network and are capable of upload of data to electronic health records of any plurality of patients seen in that hospital's clinical setting.
  • Wireless communication any method of data transfer not requiring physical wires connecting two components.
  • Methods include any plurality of zigby, Bluetooth, cellular connectivity, etc. or any combination thereof.
  • a joint kinematic analysis system functions via utilization of personal electronic device(s), internet-connected server(s), and novel sensor array technology to characterize mechanical, physiological, and spatial data representing joint motion within a dynamic environment.
  • This invention provides methods, systems and devices comprising mechanical housing, electronic hardware, firmware, and software to analyze kinematic, kinetic, and muscle activation data.
  • This invention utilizes novel spatial distribution(s) and combinations of existing sensor technology plus unique landmark algorithms to streamline data acquisition, filtering, and analysis.
  • the mechanical housing is envisioned to be a low-profile and flexible construct to allow for full range-of-motion. Leveraging the interaction between these flexible materials and current sensor technology, novel data points may be analyzed to provide novel kinetic, kinematic, and/or muscle activation information. Furthermore, these flexible materials used in conjunction with a variety of sensing modalities provide a unique opportunity to process kinetic, kinematic, and/or muscle activation data more effectively.
  • the electronic hardware comprises a central processing unit, mechanical sensing modalities (Flex, FSR, piezoelectric, strain gauges, etc.), wireless communications
  • Bluetooth Bluetooth, ZigBee, Wi-Fi, RF, etc.
  • the system described herein further comprises a communication modality to transmit the kinetic, kinematic, and/or muscle activation data and analysis to remote handheld personal electronic device(s) (PEDs) and/or internet-connected server(s).
  • PEDs remote handheld personal electronic device(s)
  • GUI graphical user interface
  • PED(s) normally refers to handheld devices or smart phones, as used herein it also includes laptops with remote wireless capabilities including Wi- Fi, 4G, etc. and/or wired connection capabilities including USB, serial, RS-232, etc.
  • the method for gathering, organizing, and analyzing the kinetic, kinematic, and/or muscle activation data is realized via a combination of firmware deployed on the joint kinematic device CPU, software deployed on the handheld PED(s), and/or internet-connected servers. Processing may be distributed in real-time amongst the PED(s), internet-connected servers, and the joint kinematic analysis device, or may be predetermined to operate on specific hardware.
  • This invention provides kinematic, kinetic, and/or muscle activation analysis of the ankle joint to characterize motion data representing joint functionality, health, movement, and disease state. While this description focuses specifically on the ankle joint, this invention may be adapted for any fibrous, cartilaginous, or synovial joint. It is envisioned that this device can be used by medical professionals and their patients in both clinical and home-based settings to monitor treatment efficacy and potentially shorten rehabilitation time. This is realized via distributed processing and data analysis interfaced with a graphical user interface that may be accessed via the PED(s) and/or online dashboard.
  • an ankle joint kinematic analysis device is worn during dynamic movement and/or stationary positioning in a clinical or home- based setting. If used in the clinical setting, a medical professional may monitor a patient's movement and interact with a graphical user interface (GUI) on a PED within communicable range to more objectively analyze ankle joint functionality and control. It is further envisioned that this device may be used to tailor treatment and rehabilitation regimes on a patient-by-patient basis.
  • the PED that is within communicable range may establish a wireless or wired connection to the device to gather and potentially assist with processing of the kinematic, kinetic, and/or muscle activation data.
  • the medical professional may remotely access data gathered by the device to track treatment and therapeutic efficacy.
  • the device uploads data and resultant metrics to a web-based interface that a medical professional may access and review.
  • FIG 1 A-F schematically illustrates the ankle joint kinematic analysis system of this invention.
  • the environment in which this system operates can involve both dynamic joint motion and stationary positioning.
  • This environment, illustrated in Figure 1 A-E, depicts a user 101 who wears the ankle joint kinematic analysis device 110-131.
  • the user 101 has PED(s) and/or HED(s) 140-143 which can establish a wireless or wired connection with the joint kinematic analysis device 110-131.
  • the electronic hardware and electronic hardware housing 121 is preferably embedded within the joint kinematic analysis device 1 10-131 , it is envisioned that the hardware is organized in a way that distributes mass as evenly as possible while also remaining low-profile.
  • the main electronic devices and electronic hardware housing 121 are preferably mounted on the medial side of the ankle sensor array housing 120. It is envisioned, however, that the main electronic devices and electronic hardware housing 121 may be housed elsewhere within the joint kinematic analysis device 110-131.
  • Sensing modalities are mounted relative to their function and are organized accordingly. Envisioned embodiments include specific sensor arrays that provide functional information based on their structure, size, and sensor organization.
  • Example positions for sensors include the ankle bend sensor array 122, the EMG sensor array and housing 1 10, heel sensor array 123, and bottom forefoot sensor array 131. More details about the ankle bend sensor array 122 can be found in Figure 8 A-B and Figure 9A-C. More details about the EMG sensor array and housing 110 can be found in Figure 5A-C and Figure 6. More details about the heel sensor array 123 can be found in Figure 8 A-B and Figure lOA-C. More details on the bottom forefoot sensor array 131 can be found in Figure 1 1A-D.
  • the main onboard processing unit in the electronic devices and electronic hardware housing 121, the CPU 250 is connected to a power source 255, a wireless communication module 253, at least one, two, or any plurality of: an Inertial Motion Unit (1MU) 240-242, an accelerometer 240-242, a gyroscope (not shown), and/or a magnetometer (not shown); at least one, two, or any plurality flex sensor 220 of arbitrary length, at least one, two, or any plurality of FSR sensors 230-232 of arbitrary size, and one, two, or any plurality of EMG sensors 210-212.
  • a wireless communication module 253 at least one, two, or any plurality of: an Inertial Motion Unit (1MU) 240-242, an accelerometer 240-242, a gyroscope (not shown), and/or a magnetometer (not shown); at least one, two, or any plurality flex sensor 220 of arbitrary length, at least one, two, or any plurality of FSR sensors 230-
  • the wireless communication module 253 establishes and maintains a connection with the PED(s) and/or HED(s) 140-143. Wireless communication then transmits biometric data and/or datasets to the PED(s) and/or HED(s) 140-143. Certain pieces of software 280 on the PED(s) and/or HED(s) 140-143 then ingest and analyze the data then organizes refined biometric data and/or datasets transmitted from the CPU 250 for display on GUI 290.
  • the wireless communication module may be replaced with a wired connection module 251 , 252, 254 that allows for the joint kinematic analysis device 110-131 to connect to the PED(s) and/or HED(s) 140-143 via hardware connection (such as USB, micro-USB, and/or the audio jack), wherein the FED and/or HED 140 - 143 and is affixed or mounted to the user 101 in some fashion.
  • a PED and/or HED 140-143 that is mounted securely to the patient (not shown) or to a separate PED and/or HED mounting pane! (not shown).
  • the PED and/or HED 140-143 may contain internal sensors such as an accelerometer, a gyroscope, a digital compass or magnetometer, GPS-tracking devices, and the like.
  • the PED and/or HED 140-143 on-board sensors may be leveraged to combine with the joint kinematic analysis device 110-131 sensors to gather and process relevant data.
  • a plurality of combinations of flex sensors of arbitrary length 220- 222 can operate within the joint kinematic analysis device 1 10-131. It is further envisioned that a plurality of combinations of FSR sensors 230-232 of arbitrary dimensions and geometry can operate within the joint kinematic analysis device 110-131.
  • the joint kinematic analysis device 110-131 deploys EMG sensors 210-212 along with an EMG signal preprocessing circuit 213-215. It is also envisioned that the joint kinematic analysis device 1 10-131 deploys flex sensors 220-222 along with a flex sensor signal preprocessing circuit 223-225. It is also envisioned that the joint kinematic analysis device 110-131 deploys FSRs 230-232 along with a FSR signal preprocessing circuit 233-235. It is also envisioned that the joint kinematic analysis device 110-131 deploys IMU sensors 240-242 along with an IMU signal preprocessing circuit 243- 245. Similar sub-circuits are envisioned for alternative sensing modalities.
  • the joint kinematic analysis device 110-131 may contain internal memory devices (such as SD, microSD, USB, etc.) functionally connected to the main electronic devices 260 for backup storage.
  • Data is relayed to an analysis software program 280 contained in PED(s) and/or HED(s) 140-143.
  • the envisioned analysis software program 280 analyzes the data and outputs clinically actionable metrics on a guided user interface (GUI) 290.
  • GUI guided user interface
  • These clinically actionable metrics are intended to give clinicians insight into the patient's symptoms to make diagnostic decisions.
  • These clinically actionable metrics can be stored locally on PED(s) and/or HED(s) 140-143. It is also envisioned that these clinically actionable metrics will be populated on a portable document file (PDF) 291.
  • PDF portable document file
  • Figure 3 illustrates a block diagram of the sequence of the method of analyzing the raw data in accordance with this invention.
  • the system operation 300 begins with the patient test performance 301.
  • the patient begins the test, resulting in Sensor stimulation, leading to biometric signal generation 309.
  • Various patient test performances may be directed and monitored by clinicians. Walking tests can provide insight into, for example, patient gait, stroke rehabilitation efficacy, neurological rehabilitation progress, and orthopedic
  • Running tests can provide insight into, for example, athletic performance, flexibility, muscle contraction speed, ground contact force levels, and dynamic pressure distributions. Furthermore, ranning tests may be conducted with controlled stopping and hopping to obtain information regarding deceleration, ground contact force time, and/or reaction time. Lateral foot scooting tests may be conducted to determine output metrics representative of balance and mobility for patients that have suffered traumatic brain injury, for example. This can be accomplished by analyzing the forefoot and heel pressure distributions throughout the lateral scoot test to characterize balance. Analysis of forefoot and heel ground contact during the lateral scoot test can provide insight into patient mobility. Forward to backward walking tests may also be conducted to monitor symptom progression associated with Parkinson's disease.
  • Forward to backward walking tests may also be conducted to evaluate crouch by tracking ankle dorsiflexion during the test, In treating crouch gait for cerebral palsy, for example, this device may be used to monitor rehabilitation progress and therapeutic efficacy of surgical or pharmaceutical intervention.
  • the CPU 250 is directed by certain pieces of software to acquire the raw biometric data acquisition 310 where raw biomctic signals are acquired from their respective sensors.
  • the joint kinematic analysis device 110-131 is acquiring certain biometric data pertaining to the physiological phenomena, kinematics, and motion of the lower limb by utilizing any plurality of EMG sensing arrays 330, flex sensor arrays 331, FSR arrays 332, or IMU arrays 333 or any combination thereof.
  • the CPU 250 acquires the onboard conditioning electrical components 31 1 to filter, rectify, isolate, or otherwise refine the raw biometric signals and/or preprocessed biometric data. More details on the onboard conditioning circuitry electrical 311 component functionalities can be found in Figure 4.
  • Raw biometric data acquisition 310 may be derived from flex sensor biometric acquisition 331 which can help detect and quantify body deformations.
  • Raw biometric acquisition 310 can additionally include IMU biometric acquisition 333 which can help detect and quantify angular and linear accelerations when undergoing dynamic movement.
  • Raw biometric data acquisition 310 can additionally include FSR biometric data acquisition 332 which can detect and quantify pressure and force applied to a body.
  • Raw biometric data acquisition 310 can additionally include EMG biometric data acquisition 330 which can detect and quantify electrical potential in muscle contraction. Details on the envisioned embodiments of the
  • aforementioned sensor arrays can be found in subsequent figures. More details about the ankle bend sensor array 122 can be found in Figure 8A-B and Figure 9A-C. More details about the EMG sensor array and housing 1 10 can be found in Figure 5 A-C and Figure 6. More details about the heel sensor array 123 can be found in Figure 8A-B and Figure lOA-C. More details on the bottom forefoot sensor array 131 can be found in Figure l lA-D.
  • the onboard conditioning electrical components 311, the CPU 250, certain pieces of software for compilation of data 312, as well as any support electrical components such as pin terminals, fuses, dampers, etc. (not shown) and power supply with battery (not shown) are collectively referred to as the motherboard 313.
  • a software program 328 installed on the PED and/or HED 140-143 handles transfer, ingestion, processing, analysis, storage, and output of the incoming data.
  • Certain pieces of software receive 320 the raw biometric data from the motherboard 313 to the PED and/or HED 140-143, certain pieces of software ingest 321 the raw biometric data into the PED and/or HED 140- 143.
  • Certain pieces of software process 322 the biometric data then certain pieces of software analyze 323 the biometric data. After analysis, the raw biometric data or biometric data is herein referred to as biometric correlation.
  • Signal processing and analysis software 322 is intended to prepare refined biometric data and/or datasets for correlation and comparison between sensor types on a sensor array.
  • Certain pieces of code correlating 323 sensor array biometric data and/or datasets seeks to combine refined biometric data and datasets between sensor types within sensor arrays to characterize ankle joint kinematics and/or electrophysiological behavior throughout the patient test. Subsequent certain pieces of software determine exact clinical metrics 324 then certain pieces of software securely store 325 these outputs On the PED and/or HED 140-143.
  • Output metric calculation 324 determines metrics mat represent, for example, muscle contraction relative to forefoot flexion or dorsiflexion events, muscle contraction relative to inversion angle, muscle contraction relative to eversion angle, heel -forefoot transition time, maximum range of motion, plantar flexion rate, forefoot flexion range of motion, heel-ground contact time, forefoot ground contact time, heel pressure distribution, forefoot pressure distribution, and/or forefoot pressure relative to forefoot flex angle. Additional output metrics may be realized based on the Sensor arrays deployed and tests the patient undergoes. These clinical metrics are then displayed on a GUI 326 that has the capability of PDF generation for filing into the patient's file history 327.
  • GUI output presentation 326 visually and quantitatively presents output metrics on PED(s) and/or HED(s) 140-143. As the patient performs the test, the GUI 326 allows the clinician to access output metrics in real-time or post-test via the GUI output presentation 326 on the PED(s) and/or HED(s) 140-143.
  • the defining characteristic of this novel software program 328 is the method of concurrent analysis using landmarks from a spectrum of biometric datasets to establish a link between physical manifestations of symptoms and probable causes of those symptoms rooted in neuromuscular dysfunction.
  • the symptoms of foot drop or foot drag can be confirmed via kinematic biometric data derived from any combination thereof IMU sensors 240-242, flex sensors 220-222, and FSR sensors 230-232 can be linked to irregular or incorrect muscle activation and/or coordination.
  • IMU sensors 240-242 IMU sensors 240-242
  • flex sensors 220-222 flex sensors 220-222
  • FSR sensors 230-232 can be linked to irregular or incorrect muscle activation and/or coordination.
  • clinician test initiation 301 may occur whereby the clinician directs the patient to restart the test, or to perform a different test.
  • certain pieces of software transmit 340 output metrics to a digital storage location. These output metrics for the specific patient that can be later accessed by the clinician.
  • the online dashboard 342 allows the clinician to manage single or multiple patient output metrics. It is additionally envisioned that a clinician may access output metrics for a multitude of patients to analyze variances on a patient-to-patient basis, evaluate treatment efficacy on a multi-patient basis, or develop treatment strategies based on previously successful approaches for similar injuries.
  • Machine learning database updates or other large data prescriptive analytics methods 341 can leverage validated output metrics to evolve prescriptive analytics methods 341 that may be used by the CPU 250 and/or the PED(s) and/or HED(s) 140-143 to more accurately or more efficiently process raw biometrics into output metrics.
  • both or only one lower limb of a person will be outfitted by a joint kinematic analysis device 110-131. Further advanced analytics can be done to assess the person's gait through coupling the data from each respective lower limb. It is also envisioned that multiple sets of data will be taken from a patient through some long term interval of time such as successive weeks or months or years such that a history of the patient's gait behavior can be kept to monitor recovery, progression of disease state, maintenance of health, etc. Referring now to Figure 4, various forms of raw biometric signal conditioning may be performed by the motherboard 313, specifically the onboard conditioning electrical components 311.
  • Onboard conditioning electrical components 311 is broadly described as a means of refining raw biometrics data and/or datasets into a data and/or datasets that can be ingested, processed, and analyzed in the software program 328 to be installed on PEDs and/or HEDs 140- 143.
  • the system operation 411 may perform onboard conditioning 411 whereby the CPU 250 accesses sensors to perform raw biometric data acquisition 410, and performs various signal manipulation techniques on the raw biometric data to create refined biometric data and/or datasets that are ultimately communicated to the PED(s) and/or HED(s) 140-143.
  • Raw biometric signal amplification 420 is used when processing raw EMG biometrics to obtain refined biometric data and/or datasets that can be more efficiently analyzed.
  • Raw biometric signal amplification 420 can be used in environments where it is difficult to determine signal variations due to low raw biometric signal amplitude.
  • Raw biometric signal filtering 430 can be used for raw biometric signals that contain high or low frequency noise components.
  • Raw biometric signal filtering 430 is useful in removing noise from raw EMG biometric signals that may contain high-frequency components that introduce error to characterization of muscle contraction data and/or datasets.
  • Raw biometric signal filtering 430 can additionally be used to remove DC error offset in an amplified signal, such as a raw EMG biometric signal.
  • Raw biometric signal rectification 440 is useful in converting negative signal values of a raw EMG biometric signal, for example, into positive signal values which allows for simpler and more efficient processing by the CPU 250.
  • Raw biometric signal isolation 450 can be used to eliminate or reduce operating disturbances that may occur as a result of raw biometric signal amplification 420.
  • the sensor connection may allow for interactions between the amplifier power source and the main electronic devices 310, potentially leading to device failure.
  • Optical or magnetic isolation may be used to prevent the amplifier circuit from directly influencing the main electronics devices.
  • Raw biometric signal conversion 460 can be used to convert analog biometric signals into digital quantities that the CPU 250 may then analyze and process to create refined biometric data and/or datasets.
  • the raw flex biometric signal for example, consists of an analog voltage that is read from a voltage divider circuit where the analog voltage drop across flex sensor arrays 220-222 is a result of the resistance generated upon flexion.
  • FIG. 5 A-C illustrates an example embodiment of various EMG sensor array distributions on the lower leg and foot may be used to acquire electrophysiological data on lower leg kinematics.
  • a plurality of EMG sensors 530 are distributed across various muscle groups of the lower leg 510-512 in what is collectively referred to previously as the EMG sensor array and housing 110, This provides information regarding muscle contraction and coordination during dynamic movement of the lower leg and foot.
  • An array of one, two, or any plurality of EMG electrodes arranged in an arbitrary geometry provides a raw muscle contraction biometrics representing the gastrocnemius (lateral head and medial head) 511.
  • the EMG sensor array and housing 110 may contain a plurality of EMG electrodes arranged in an arbitrary geometry allows raw muscle contraction biometric signals to be obtained despite with highly varying positions of the gastrocnemius 511 across a patient population.
  • the arbitrary EMG electrode within this array that produces the best signal most likely located in closest proximity to the center of the muscle 533 is then attached to the gastrocnemius lead wire 542. This wire relays the raw muscle contraction biometrics representing the gastrocnemius 511 to the onboard conditioning electrical components 31 1 on the motherboard 313.
  • An array of one, two, or any plurality of EMG electrodes arranged in an arbitrary geometry provides a raw muscle contraction biometric signals representing the anterior tibialis 510.
  • This plurality of EMG electrodes arranged in an arbitrary geometry allows raw muscle contraction biometrics to be obtained despite with highly varying positions of the anterior tibialis 510 across a patient population.
  • the arbitrary EMG electrode within this array that produces the best signal most likely located in closest proximity to the center of the muscle 531 is then attached to the anterior tibilialis lead wire 541. This wire relays the raw muscle contraction biometric signals representing the anterior tibialis 510 to the onboard conditioning electrical components 311 on the motherboard 313.
  • An array of one, two, or any plurality of EMG electrodes arranged in an arbitrary geometry provides a raw muscle contraction biometrics representing the soleus 512.
  • This plurality of EMG electrodes arranged in an arbitrary geometry allows raw muscle contraction biometrics to be obtained despite with highly varying positions of the soleus 512 across a patient population.
  • the arbitrary EMG electrode within this array that produces the best signal most likely located in closest proximity to the center of the muscle 534 is then attached to the anterior tibilialis lead wire 543. This wire relays the raw muscle contraction biometric signals representing the soleus 512 to the onboard conditioning electrical components 311 on the motherboard 313.
  • the EMG ground reference electrode 532 may be used when gathering raw biometric data to quantify muscle contraction behavior, and may be used as a reference for at least one other EMG sensor 530 within the joint kinematic analysis device 110- 131. It is envisioned that these EMG electrodes 530 can be incorporated into an arbitrary elastic fabric or textile mesh 520 that is form fitting to the lower leg through any method including but not limited to fastened, embedded, inserted, etc. or any combination thereof.
  • the raw biometric signals from these various sensor arrays can be analyzed and processed to provide output metrics that relate muscle contraction to spatial movement, and pressures or forces experienced by the ankle joint. Additionally, various combinations of these EMG sensor arrays can be used to provide output metrics relative to length of freezing episodes and/or co-contraction in an effort to better diagnose and track Parkinson's disease progression. Specifically, EMG sensor placement on opposing muscle groups can be analyzed to provide additional output metrics relative to freezing episodes and/or co- contraction. These data can also provide additional output relative to muscle atrophy or spacicity. Data from EMG sensors may also be monitored over an arbitrary time frame to determine if irregularities in muscle activation or coordination is rhythmic or sporadic or any combination thereof.
  • EMG sensors 530 may be those of the traditional electrode type seen in literature such as making use of an adhesive gel surface EMG or textile or embedded fabric based EMG surface electrode or any combination thereof. Additionally, wearable electrodes may be used that consist of conductive fabric that can transmit a raw biometric data to any necessary preprocessing circuits, and subsequently the CPU 250. Electrodes may also be fabricated via additive manufacturing, or alternative 3d printing techniques. This can be accomplished via the use of electrically conductive materials that can transmit a raw biometric data to any necessary preprocessing circuits, and subsequently the CPU 250.
  • FIG. 6 illustrates a block diagram to relay certain data from any plurality of EMG sensors 210-212 to the electronic devices and electronic hardware housing 121 for further conditioning.
  • EMG sensor electrodes 210-212 as well as a ground reference electrode 613 may be bundled together in a wiring conduit with a snap connector 620 or any version of a wiring fastener to other electrical components.
  • EMG sensor electrodes 210-212 and the ground reference electrode 613 and the EMG sensor array housing 520 are collectively referred to as previously mentioned the EMG sensing array 1 10.
  • This wiring bundle 620 may then be directly connected to the mother board 313 for further onboard conditioning and data acquisition into the GPU 250.
  • Figure 7 A illustrates an example of biometric data derived from the EMG sensor array for an arbitrary anterior tibialis muscle 510 throughout an arbitrary gait cycle of a human after certain pieces of software process 322 data derived from the EMG sensing array 630.
  • Certain pieces of software process and graph 322 processed anterior tibialis EMG biometric data 710 on the GUI 326 for the clinician to infer muscle activity and coordination.
  • the processed tibialis anterior EMG biometric 710 is zero volts due to the lack of muscle contraction when a patient is in a standing position.
  • the processed tibialis anterior EMG signal 710 rises to a maximum indicative of the peak muscle contraction achieved during any state of motion requiring dorsiflexion or inversion of the foot.
  • peak anterior tibialis muscle contraction 711 may occur at and/or between the time duration of heel strikes 1510 to midstances 1512.
  • Certain pieces of software that process 322 may utilize a normalized threshold voltage 712 to determine legitimate muscle activations or determine relatively weak muscle activations.
  • Figure 7B illustrates an example of biometric data derived from the EMG sensor array for an arbitrary gastrocnemius muscle 511 throughout an arbitrary gait cycle of a human after certain pieces of software process 322 data derived from the EMG sensing array 630.
  • Certain pieces of software process and graph 322 processed gastrocnemius EMG biometric data 720 on the GUI 326 for the clinician to infer muscle activity and coordination.
  • the processed gastrocnemius EMG biometric 720 is near zero volts due to the lack of muscle contraction when a patient is in a standing position however is nonzero because gastrocnemius 51 1 activation is necessary to stay standing for an arbitrary human with normal gait.
  • the processed gastrocnemius EMG signal 720 rises to a maximum indicative of the peak muscle contraction 721 achieved during any state of motion requiring plantar flexion of the foot.
  • peak gastrocnemius muscle contraction 721 may occur at and/or between the time duration of midstances 1512 to toe offs 1514.
  • Certain pieces of software that process 322 may utilize a normalized threshold voltage 722 to determine legitimate muscle activations or determine relatively weak muscle activations.
  • Figure 7C illustrates an example of biometric data derived from the EMG sensor array for an arbitrary soleus muscle 512 throughout an arbitrary gait cycle of a human after certain pieces of software process 322 data derived from the EMG sensing array 630.
  • Certain pieces of software process and graph 322 processed soleus EMG biometric data 730 on the GUI 326 for the clinician to infer muscle activity and coordination.
  • the processed soleus EMG biometric 730 is near zero volts due to the lack of muscle contraction when a patient is in a standing position however may be nonzero because soleus 512 activation is necessary to stay standing for an arbitrary human with normal gait.
  • the processed soleus EMG signal 730 rises to a maximum indicative of the peak muscle contraction 731 achieved during any state of motion requiring plantar flexion of the foot.
  • peak soleus muscle contraction 731 may occur at and/or between the time duration of midstances 1512 to toe offs 1514.
  • Certain pieces of software that process 322 may utilize a normalized threshold voltage 732 to determine legitimate muscle activations or determine relatively weak muscle activations. Further details on EMG biometric data analysis an irregularity detection can be found in Figure 16.
  • FIG. 8A-B illustrates an example embodiment of an ankle sensor array 800.
  • a combination of an ankle bend sensor 122 array, a heel sensor array 123, and an electronic devices and electronic hardware housing 121 may be used to collect biometric data during lower limb movement of an arbitrary human.
  • the ankle bend sensor 122 array, heel sensor array 123, and electronic devices and electronic hardware housing 121 are inserted, fastened, embedded, clipped, or somehow else secured or any combination thereof into an ankle sensor array housing 810 made of an arbitrary elastic fabric or textile mesh.
  • the ankle bend sensor array 122 may be secured to the ankle sensor array housing 120 through use of elastic fabric or textile mesh conduits 841.
  • heel sensor array 123 may be secured to the ankle sensor array housing 810 through the combined use of elastic fabric or textile mesh conduit 821 and latch connection 822 to the electronic devices and electronic hardware housing 121.
  • Figure 9A illustrates a detailed example embodiment of the ankle bend sensor array 122.
  • Figure 9C illustrates an example system block diagram of the ankle bend sensor array 122 and may be used to conceptually simplify the ankle bend sensor array 122.
  • the ankle bend sensor array 122 may contain one, two, or any plurality of flex sensors of arbitrary length 220 distributed circumferentially upon the patient's lower leg reaching down to the top of the foot to capture changes in range of motion upon the ankle during movement.
  • the ankle sensor array may contain one, two, or any plurality of IMUs 240.
  • This provides a multitude of flex sensors to monitor for robust, redundant processing as well as a gradient for the measured signal that represents the upward and downward flex of the ankle.
  • Any plurality of flex sensors 220 or plurality of IMUs 240 or combination thereof can be inserted, fastened, embedded, integrated, or any combination thereof into a flexible mechanical housing 920 of arbitrary material.
  • Signal originating from any plurality of flex sensors 220 or plurality of IMUs 240 or combination thereof are relayed through a wire bundle 931.
  • This wiring bundle 931 may then be connected to the electronic devices and electronic hardware housing 121 in Figure 9B through a cut-out 940 through use of a latch connector 930.
  • FIG 10A illustrates a detailed example embodiment of the heel sensor array 123.
  • Figure 10C illustrates an example system block diagram of the heel sensor array 123 and may be used to conceptually simplify the heel sensor array 123.
  • a plurality of FSR of arbitrary size and geometry sensors 230 are distributed across the surface of the heel. This provides a multitude of FSR sensors to monitor pressure distribution across the surface of the heel. This can provide insight into the pressures the heel is subject to during loading.
  • Any plurality of FSR sensors 230 can be inserted, fastened, embedded, integrated, or any combination thereof into a flexible mechanical housing 1011 of arbitrary material. Signal originating from any plurality of FSR sensors 230 are relayed through a wire bundle 1031.
  • This wiring bundle 1021 may then be connected to the electronic devices and electronic hardware housing 121 in Figure 10B through a cut-out 1031 through use of a latch connector 1020.
  • Figure 11A-B illustrates an example embodiment of a forefoot sensor array 131 and the forefoot sensor array housing 130.
  • a combination of a forefoot sensor array 131 and an electronic devices and electronic hardware housing 121 may be used to collect biometric data during lower limb movement of an arbitrary human.
  • the forefoot sensor array 131 and electronic devices and electronic hardware housing 121 are inserted, fastened, embedded, clipped, or somehow else secured or any combination thereof into a forefoot sensor array housing 130 made of an arbitrary elastic fabric or textile mesh.
  • the bottom forefoot sensor array 131 may be secured to the forefoot sensor array housing 130 through use of elastic fabric or textile mesh conduits 1 151.
  • Figure l lC illustrates a detailed example embodiment of the bottom forefoot sensor array 131.
  • Figure 1 ID illustrates an example system block diagram of the bottom forefoot sensor array 131 and may be used to conceptually simplify the forefoot sensor array 131.
  • a plurality of FSR of arbitrary size and geometry sensors 231-232 are distributed across the bottom forefoot. This provides a multitude of FSR sensors to monitor pressure distribution across the surface of the bottom forefoot. This can provide insight into the pressures the bottom forefoot is subject to during loading. Any plurality of FSRs 231-232 can be inserted, fastened, embedded, integrated, or any combination thereof into a flexible mechanical housing 1 120 of arbitrary material.
  • This wiring bundle 1131 may then be connected to the electronic devices and electronic hardware housing 121 in Figure 10B through a cut-out 1031 or to the electronic devices and electronic hardware housing 121 in Figure 9B through a cut-out 940.
  • FIG. 12A illustrates an example of biometric data correlation from FSR sensor array of a heel throughout an arbitrary gait cycle of a human.
  • the biometric data correlation from FSR sensor at the heel 1201 is generated when a user 1500 engages in an arbitrary gait cycle 1510-1516.
  • One biometric data correlation of the FSR sensor at the heel 1201 is generated per device outfitted on the user 1500.
  • the initial frame of reference of any arbitrary gait cycle is during the moment of heel strike 1510.
  • there may be initial contact of the heel of the user 1500 to the ground thus creating a ground reaction force that stimulates the FSR sensor at the heel 230 begin to transmit a non-zero voltage output.
  • the ground reaction force generated between the heel of the user 1500 and contact with the ground begins to grow in magnitude thereby stimulating a larger non-zero voltage output from the FSR sensor at the heel 230.
  • the FSR sensor of the heel 230 may not be stimulated thereby outputting a zero value voltage 121 1.
  • Heel strike markers 1220 corresponding to the timing of a heel strike 1510 of an arbitrary normal human gait cycle may be used in the data correlation and analysis 323 portion of the software program 328 installed on the FED and/or HED 140-143.
  • FIG 12B illustrates an example of biometric data correlation FSR sensor array of a forefoot throughout an arbitrary gait cycle of a human.
  • the biometric data correlation of the FSR sensor at the medial forefoot 1251 and raw biometric correlation of the FSR sensor at the distal forefoot 1261 are generated when a user 1500 engages in an arbitrary gait cycle 1510-1516.
  • One raw biometric correlation of the FSR sensor at the medial forefoot 1251 and raw biometric correlation of the FSR sensor at the distal forefoot 1261 are generated per device outfitted on the user 1500.
  • an arbitrary gait cycle it can be assumed that the initial frame of reference of any arbitrary gait cycle is during the moment of heel strike 1510.
  • the forefoot of the user 1500 has no contact with the ground thus creating a ground reaction force of zero that stimulates the FSR sensor at the medial forefoot 231 and distal forefoot 232 to transmit zero voltage outputs 1270.
  • Initial contact of the forefoot of the use 1500 with the ground is engaged at footflat 151 1 and carries through midstance 1512 into pushoff 1513 phases of an arbitrary gait cycle. During these phases, the ground reaction force generated between the forefoot of the user 1500 and contact with the ground begins to grow in magnitude thereby stimulating a larger non-zero voltage output from the FSR sensor at the both the medial forefoot 231 and distal forefoot 232.
  • Figure 13 illustrates an example of biometric data correlation interpretation of a biometric data correlation FSR sensor array of a heel 1201, biometric data correlation FSR sensor of the medial forefoot 1251, and biometric data correlation of the distal forefoot 1261 overlaid upon one another throughout an arbitrary gait cycle of a human truncated to one singular step defined as one cycle of all the phases 1510-1516 starting with heel strike 1510.
  • biometric data correlation interpretation of a biometric data correlation FSR sensor array of a heel 1201, biometric data correlation FSR sensor of the medial forefoot 1251, and biometric data correlation of the distal forefoot 1261 are superimposed upon one another as depicted in Figure 13 and toe off markers 1240 and heel strike markers 1220 corresponding to the timing of a toe off 1514 and heel strike 1510, respectively, of an arbitrary normal human gait cycle are utilized to truncate a user's 1500 gait cycle into sequential, unique steps;
  • Figure 14A illustrates an example of raw velocity correlation IMU sensor array throughout an arbitrary gait cycle of a human 140.1 derived from raw accelerometer data correlation (not shown).
  • Gryometer and/or magnetometer raw biometric correlation may also be utilized to derive kinematic models describing movement of the lower limb of the user 1500.
  • Raw velocity correlation IMU sensor array 1401 may contain landmark features including zero value voltage outputs 1450 indicating no movement or change in displacement. Areas of positive value voltage outputs 1410 indicate movement or change in displacement in the positive direction of the axis. Local maxima and minima in these positive value voltage outputs indicate changes in acceleration correlated with changes in the mode of displacement. Successive increases in positive value output voltages 1410 indicate increase in rate of change of displacement in the positive direction of the axis.
  • Successive decreases in positive value output voltages 1410 indicate decreased rate of change in the displacement in the positive direction of the axis.
  • Areas of negative value voltage outputs 1440 indicate movement or change in displacement in the direction of motion. Local maxima and minima in these negative value voltage outputs indicate changes in acceleration correlated with changes in the mode of displacement.
  • Successive decreases in negative value output voltages 1440 indicate increase in rate of change of displacement in the negative direction of the axis.
  • Successive increases in negative value output voltages 1440 indicate decreased rate of change in the displacement in the negative direction of the axis.
  • Inflection points 1420 are defined as a singular, transient zero value voltage output preceded by a non- zero value voltage output and proceeded by a non-zero value voltage output and are indicative of a change in direction of motion.
  • Example analysis of the velocity correlation from an IMU sensor array 1401 may proceed as follows for vertical displacement of the lower limb of the user 1500. Heel strike 1510 and heel off 1513 or toe off 1514 is determined by analyzing periods of zero value outputs 1450, positive value voltage outputs 1410, inflection points 1420, and negative value voltage outputs 1440. During periods of zero value outputs 1450, the user 1500 is exhibiting no displacement of the lower limb corresponding to the phases of footflat 151 1 and midstance 1512. When positive value outputs 1410 begin to be present, this may indicate push off 1513. These positive value outputs may persist and peak through acceleration 1514 and continue to be positive however these outputs may decrease during midswing 1515.
  • the voltage output will be zero indicating an inflection point 1420.
  • This inflection point represents the apex or maximum height of the foot during the swing phase of a normal, arbitrary human gait.
  • the voltage output will be negative indicating deceleration 1516 until heel strike 1510 thereby restarting the cycle at a zero voltage output.
  • the heel strike marker 1220 and toe off marker 1240 arid corresponding time stamps are utilized in further data correlation and analysis 323 portion of the software program 328 installed on the PED and/or HED 140-143.
  • Velocity data correlation IMU sensor array 1401 may be determined for each respective axis of motion. There may be up to 9 degrees of freedom associated with each IMU. Velocity data correlation IMU sensor array 1401 may be obtained through the use of calculus integration of the acceleration correlation and/or use of a kinematic model that may make use of gyrometer and/or magnetometer data or any other landmark sensory data in the joint kinematic analysis device 1 10- 131 described or any combination thereof. Any combination of known signals analysis and/or kalman filters or other signal filtering methods can be used to clean and produce easier to use data for analysis.
  • FIG 148 illustrates an example of biometric data correlation flex sensor array throughout an arbitrary gait cycle of a human.
  • Biometric data correlation of the flex sensor array 1402 can be used to alternatively determine gait cycle progression by looking at the angle of deflection of the ankle.
  • Local maxima and minima 1450 may represent periods of small angle deflection and large angle deflections.
  • Large angle of ankle deflection may indicate plantar flexion in phases such as push off 1513.
  • Small angle of ankle deflection may indicate dorsiflexion in phases such as deceleration 1516 or heel strike 1510 or midstance 1512.
  • These landmark data can be used to aid in analysis of other systems as well as determine range of motion of the ankle.
  • Figure 15A illustrates an arbitrary gait cycle of a human.
  • One step of an arbitrary gait cycle of a human is defined as one full cycle of heel strike 1510, foot flat 151 1, midstance 1512, push off (heel off) 1513, acceleration (toe off) 1514, midswing 1515, and deceleration 1516.
  • Figure 15B illustrates an example data processing method for the data correlation and analysis 323 portion of the software program 328 installed on the PED and/or HED 140-143.
  • this biometric data correlation and analysis 323 portion of the software program 328 installed on the PED and/or HED 140-143 contains certain pieces of software that ingest 321 raw biological correlation data from FSR sensors 230-232, IMUs 240-242, flex sensors 220-222, and/or EMG sensors 210-212 or any combination thereof. It is envisioned that each raw biometric data and/or datasets may be preprocessed via software and/or hardware. After data ingestion 321, certain pieces of software process 322 the raw data to obtain biometric data correlation. Biometric data correlation derived from FSRs 1201, 1251, 1261 are indicative of GRP. Velocity correlation derived from IMUs 1401 are indicative of kinematics of the lower limb.
  • Biometric data correlation derived from flex sensors 1402 are indicative of range of motion and angle between shin and foot.
  • EMG biometric data correlation derived from EMG sensors 1612, 1622 are indicative of muscle contraction. All correlation graphs are graphed on the same time scale to enable viewing of all data as it simultaneously occurs such that landmark events of all sensor data can be viewed for temporal comparison.
  • biometric data processing 322 certain pieces of software determine landmark features 1540 in each of the correlation graphs .
  • the phases of gait 1510-1516 are determined for every given time interval of the data through analysis of landmark features of the biometric data correlation derived from FSRs 1201, 1251, 1261 are indicative of GRF. These time stamps are envisioned to be labeled throughout the concurrent correlation graphs with global markers demarking the different phases of gait 1541.
  • Velocity correlation derived from IMUs 1401 are indicative of kinematics of the lower limb 1542.
  • Biometric data correlation derived from flex sensors 1402 are indicative of range of motion and angle between shin and foot herein referred to as shank angle 1543.
  • Biometric data correlation derived from EMG sensors 1612, 1622 are indicative of muscle activation from various muscles throughout gait. Average dynamic thresholds 1544 are determined throughout the curves for determination of authentic muscle contraction.
  • each step of the correlation graphs is truncated into the different phases using the global markers 1541.
  • certain pieces of software detect irregularities 1560 that deviate from normal gait patterns. These irregularities can include but are not limited to foot drag 1561, foot drop 1562, spasticity 1563, co-contraction 1564, and random or rhythmic irregularities 1565 or any combination thereof. Foot drag 1561 may be detected by consistent low displacement responses in the positional correlation graph 1401 derived from an IMU with data pertinent to the vertical axis of motion during the swing phases of the gait cycle.
  • Foot drop 1562 may be detected by sharp and sudden decreases in displacement responses in the positional correlation graph 1401 derived from an IMU with data pertinent to the vertical axis of motion during the swing phases of the gait cycle. Spacicity may be detected through irregular or abnormally weak EMG biometric correlation plots 1612, 1622 with low activation threshold or sporadic activation thresholds 1544. Co-contraction 1564 may be detected by two EMG responses exceeding threshold in an overlapping time frame 1632, 1633 as depicted in Figure 16 caused by two antagonistic muscles contracting at once. This co-contraction may result in foot drag 1561 and/or foot drop 1562. Furthermore, irregularities can be determined to be random or rhythmic 1565.
  • More metrics may be derived from manipulation of these data sets that have clinically actionable outcomes. Monitoring of these irregularities long term to measure efficacy of treatment or progression of disease states may also be utilized to improve user outcomes. Risk identifiers or markers can also be determined to detect early onset of neuromuscular dysfunction.
  • Figure 16 is example analyzed EMG data to determine co-contraction during an arbitrary gait cycle. These graphs are individual EMG biometric data superimposed on one another on the same time scale.
  • the anterior tibialis biometric data 712 and the gastrocnemius biometric data 722 are used for this arbitrary analysis.
  • the biometric correlation graph of the anterior tibialis 1612 and gastrocnemius 1622 are produced by the software program 328 installed on the PED and/or HED 140-143 through truncating the user 1500 gait into individual steps.
  • the soleus correlation graph (not shown) will resemble the gastrocnemius correlation graph 1622.
  • average voltage outputs are determined and a dynamic average threshold is established for each truncated data set of each muscle.
  • an average dynamic threshold for the gastrocnemius 1611 and an average dynamic threshold for the anterior tibialis 1621 is determined. Any voltage output below the average dynamic threshold is interpreted as no contraction of the muscle. Any voltage output above tile average dynamic threshold is interpreted as contraction of the muscle.
  • peak values 1610 in the gastrocnemius correlation graph 1612 are indicative of gastrocnemius contraction.
  • peak values 1620 in the anterior tibialis correlation graph 1622 are indicative of anterior tibialis contraction. Because the gastrocnemius 511 and anterior tibialis 510 are antagonistic muscles, they should never be contracting at the same time. Overlapping contraction cycles of these muscles may cancel each muscle's intended resulting action. Such events are recorded in EMG correlation graphs 1612, 1622 by positive readings of contraction of both the gastrocnemius 1631 and the anterior tibialis 1630 during the same time interval 1632, 1633 and is herein referred to as co- contraction. The beginning of the co-contraction is demarked visually in the correlation graph with an arbitrary marker 1632 and the end of the co-contraction period is demarked visually in the correlation graph with a separate arbitrary marker 1633.
  • Figure 17 is example analyzed IMU data to model velocity and displacement profile in the positive direction of walking during an arbitrary gait cycle.
  • This example illustrates only one of the three axes of motion that can be analyzed for motion.
  • accelerometer data from an IMU 240-243 is ingested and processed in the software program 328.
  • This accelerometer data is processed into a velocity correlation graph 1720 and a displacement correlation graph 1710.
  • Toe off markers 1240 and heel strike markers 1220 are applied at known time intervals.
  • Zero value outputs 1723 in the velocity correlation graph 1720 corresponds to periods of no displacement 1710 in the lower limb in which no motion is observed because the lower limb is in the stance phases of the gait cycle which include the end of heel strike 1510, footflat 1511, midstance 1512, and the beginning of push off 1513.
  • displacement is observed and demarked by the toe off marker 1240 in which motion is initiated by the lower limb.
  • This causes the velocity to increase in the positive direction of the axis thus eliciting a nonzero response that increases in amplitude 1722 in the velocity correlation graph that peaks 1722 during midswing 1515 indicating maximum velocity of lower limb swinging forward.
  • the lower leg moves through deceleration 1516 that sees a decrease in amplitude of the velocity correlation graph 1720 back to a zero value output 1723 indicating another heel strike 1510.
  • the displacement correlation graph 1710 is obtained through a combination of calculus integration of the velocity correlation graph 1720 and/or other kinematic models modeling lower limb gait kinematics as well as processes designed to make more accurate the displacement correlation graph 1710 such as but not limited to kalman filters, peak to peak smoothing curves, or other Gaussian approaches to better extrapolate or interpolate data points or any combination thereof.
  • Time intervals of no motion in the displacement correlation graph 1710 are demarked by intervals of no change in response 1711.
  • Time intervals corresponding to the lower leg swinging forward thus displacing forward are indicated by intervals of non-constant output 1712 that correspond to non zero values 1722 in the velocity correlation graph 1720.
  • Analysis can be used to determine inconsistent gait patterns such as irregular step distance, step frequency, irregular timing of the gait cycle, as well as foot drop and foot drag.
  • This same analyses can also be applied to other axes of motion to produce displacement and velocity profiles in the other two axes of direction to further provide more insight in the kinematics of the lower limb.
  • This analysis is coupled with other analyses in the software program 328 to obtain kinematic data that is objectively related back to the muscle coordination.
  • Analysis of the flex sensors individually and/or as a whole within the ankle bend array 122 can provide information regarding the three-dimensional movement of foot relative to the lower leg.
  • Analysis of acceleration data derived from IMU 240-242 correlated to the flex data derived from flex sensor 220-222 and muscle activation from the EMG sensor array 210-212 can provide insight into the relationship between muscle activation and kinematics relative to the lower leg. It is envisioned that correlation and analysis of spatial and muscle activation data can help a medical professional better treat a patient who may possess reduced range of motion only in certain planes or directions. This may potentially help the medical professional determine with better accuracy which ligaments, joints, bones, and/or muscles may be damaged as well as help the medical professional track treatment efficacy and rehabilitation.

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Abstract

L'invention concerne un système, un procédé et un dispositif d'analyse cinématique d'articulation de cheville pour caractériser un utilisateur de mouvement d'articulation de cheville dans un environnement dynamique et fournir un retour d'information ultérieure à un utilisateur.
PCT/US2018/014944 2017-01-24 2018-01-24 Procédé, système et dispositif d'analyse de cinématique d'articulation de cheville Ceased WO2018140429A1 (fr)

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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200037926A1 (en) * 2017-03-09 2020-02-06 BEKA Hospitec GmbH Gait Training Method with a Sensor-Based Reduction or Variation of the Weight Load
US20200289009A1 (en) * 2018-04-25 2020-09-17 Instituto Tecnol0Gico Y De Estudios Superiores De Monterrey System, method and apparatus for assessing and monitoring muscle performance with self-adjusting feedback
CN113268141A (zh) * 2021-05-17 2021-08-17 西南大学 一种基于惯性传感器和织物电子的动作捕获方法及装置
US20220000429A1 (en) * 2020-07-03 2022-01-06 Nec Corporation Estimation apparatus, estimation method, and non-transitory computer-readable recording medium
WO2022015877A1 (fr) * 2020-07-14 2022-01-20 Howmedica Osteonics Corp. Analyse dynamique d'articulation pour remplacement d'articulation
US11510035B2 (en) 2018-11-07 2022-11-22 Kyle Craig Wearable device for measuring body kinetics
US20230337791A1 (en) * 2020-07-13 2023-10-26 Lifespark Technologies Private Limited A gait moderation aiding system

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060085047A1 (en) * 2004-10-18 2006-04-20 Unsworth John D Neuromuscular electrical stimulation of the foot muscles for prevention of deep vein thrombosis and pulmonary embolism with motion detection control
US20100110169A1 (en) * 2008-07-24 2010-05-06 Noah Zerkin System and method for motion capture
US20150366504A1 (en) * 2014-06-20 2015-12-24 Medibotics Llc Electromyographic Clothing
US20160206242A1 (en) * 2013-07-31 2016-07-21 Sensoria Inc Methods and systems for data collection, analysis and formulation of user-specific feedback; use of sensing systems as input devices
WO2016196801A1 (fr) * 2015-06-02 2016-12-08 Battelle Memorial Institute Manchon neural pour enregistrement, détection et stimulation neuromusculaire
CA2913304A1 (fr) * 2015-11-26 2017-05-26 Bakker, Ryan Methode et systeme de surveillance et analyse de blessures et maladies des articulations

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060085047A1 (en) * 2004-10-18 2006-04-20 Unsworth John D Neuromuscular electrical stimulation of the foot muscles for prevention of deep vein thrombosis and pulmonary embolism with motion detection control
US20100110169A1 (en) * 2008-07-24 2010-05-06 Noah Zerkin System and method for motion capture
US20160206242A1 (en) * 2013-07-31 2016-07-21 Sensoria Inc Methods and systems for data collection, analysis and formulation of user-specific feedback; use of sensing systems as input devices
US20150366504A1 (en) * 2014-06-20 2015-12-24 Medibotics Llc Electromyographic Clothing
WO2016196801A1 (fr) * 2015-06-02 2016-12-08 Battelle Memorial Institute Manchon neural pour enregistrement, détection et stimulation neuromusculaire
CA2913304A1 (fr) * 2015-11-26 2017-05-26 Bakker, Ryan Methode et systeme de surveillance et analyse de blessures et maladies des articulations

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200037926A1 (en) * 2017-03-09 2020-02-06 BEKA Hospitec GmbH Gait Training Method with a Sensor-Based Reduction or Variation of the Weight Load
US20200289009A1 (en) * 2018-04-25 2020-09-17 Instituto Tecnol0Gico Y De Estudios Superiores De Monterrey System, method and apparatus for assessing and monitoring muscle performance with self-adjusting feedback
US11633140B2 (en) * 2018-04-25 2023-04-25 Instituto Tecnológico y de Estudios Superiores de Monterrey System, method and apparatus for assessing and monitoring muscle performance with self-adjusting feedback
US11510035B2 (en) 2018-11-07 2022-11-22 Kyle Craig Wearable device for measuring body kinetics
US20220000429A1 (en) * 2020-07-03 2022-01-06 Nec Corporation Estimation apparatus, estimation method, and non-transitory computer-readable recording medium
US20230337791A1 (en) * 2020-07-13 2023-10-26 Lifespark Technologies Private Limited A gait moderation aiding system
WO2022015877A1 (fr) * 2020-07-14 2022-01-20 Howmedica Osteonics Corp. Analyse dynamique d'articulation pour remplacement d'articulation
CN113268141A (zh) * 2021-05-17 2021-08-17 西南大学 一种基于惯性传感器和织物电子的动作捕获方法及装置
CN113268141B (zh) * 2021-05-17 2022-09-13 西南大学 一种基于惯性传感器和织物电子的动作捕获方法及装置

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