Wearable gait monitoring apparatus, systems, and related methods

• US 10,448,867 B2
• Filed: 11/21/2014
• Issued: 10/22/2019
• Est. Priority Date: 09/05/2014
• Status: Active Grant

• Current Assignee: Beijing Zitiao Network Technology Company Limited (ByteDance Ltd.)
• Original Assignee: Vision Service Plan (VSP Global, Inc.)
• Inventors: Sales, Jay William, Klosinski, Jr., Richard Chester, Workman, Matthew Allen, Murphy, Meghan Kathleen, Steen, Matthew David
• Primary Examiner(s): Towa, Rene T
• Application Number:
  US14/550,406
• Publication Number:
  US 20160066820A1
• Time in Patent Office: 1,796 Days
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 2562/0223 Magnetic field sensors
• CPC Class Codes: A61B 2562/0257 Proximity sensors
• CPC Class Codes: A61B 2576/00 Medical imaging apparatus involving image processing or analysis A61B1/00009, A61B6/52 and A61B8/52 take precedence; image processing per se G06T; details of algorithms for analysing medical images G06T7/0012
• CPC Class Codes: A61B 3/112 for measuring diameter of pupils
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/1032 Determining colour for diagnostic purposes measuring colour in general G01J3/46
• CPC Class Codes: A61B 5/1103 Detecting eye twinkling
• CPC Class Codes: A61B 5/1114 Tracking parts of the body
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/1128 using image analysis A61B5/1127 takes precedence
• CPC Class Codes: A61B 5/117 : Identification of persons methods or arrangements for recognising patterns, e.g. fingerprints, G06F18/00, G06V40/00; identification of persons by analysing their voice or speech G10L17/00
• CPC Class Codes: A61B 5/1176 : Recognition of faces
• CPC Class Codes: A61B 5/14552 : Details of sensors specially adapted therefor
• CPC Class Codes: A61B 5/165 : Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/318 : Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 : Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/4076 : Diagnosing or monitoring particular conditions of the nervous system A61B5/4821, A61B5/4824 take precedence
• CPC Class Codes: A61B 5/4266 : sweat secretion
• CPC Class Codes: A61B 5/443 : Evaluating skin constituents, e.g. elastin, melanin, water
• CPC Class Codes: A61B 5/486 : Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/4884 : inducing physiological or psychological stress, e.g. applications for stress testing
• CPC Class Codes: A61B 5/6803 : Head-worn items, e.g. helmets, masks, headphones or goggles
• CPC Class Codes: A61B 5/7246 : using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7278 : Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7282 : Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 7/04 : Electric stethoscopes microphones, acoustic transducers therefor H04R
• CPC Class Codes: A61F 2/76 : Means for assembling, fitting or testing prostheses, e.g. for measuring or balancing, e.g. alignment means
• CPC Class Codes: A61F 2002/7695 : Means for testing non-implantable prostheses for testing implantable prostheses A61F2240/008; for implanting artificial joints A61F2/468
• CPC Class Codes: A63B 24/0062 : Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: G02C 11/10 : Electronic devices other than hearing aids
• CPC Class Codes: G06F 18/22 : Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 21/35 : communicating wirelessly
• CPC Class Codes: G06F 2218/00 : Aspects of pattern recognition specially adapted for signal processing
• CPC Class Codes: G06V 20/10 : Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59
• CPC Class Codes: G06V 40/18 : Eye characteristics, e.g. of the iris
• CPC Class Codes: G06V 40/19 : Sensors therefor
• CPC Class Codes: G06V 40/197 : Matching; Classification
• CPC Class Codes: G06V 40/25 : Recognition of walking or running movements, e.g. gait recognition
• CPC Class Codes: G07C 9/37 : using biometric data, e.g. fingerprints, iris scans or voice recognition
• CPC Class Codes: G08B 21/02 : Alarms for ensuring the safety of persons
• CPC Class Codes: G08B 21/0423 : detecting deviation from an expected pattern of behaviour or schedule
• CPC Class Codes: G08B 21/0461 : integrated or attached to an item closely associated with the person but not worn by the person, e.g. chair, walking stick, bed sensor
• CPC Class Codes: G08B 21/0476 : Cameras to detect unsafe condition, e.g. video cameras
• CPC Class Codes: G09B 19/0092 : Nutrition
• CPC Class Codes: G09B 5/00 : Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F
• CPC Class Codes: G09B 5/06 : with both visual and audible presentation of the material to be studied
• CPC Class Codes: G16H 20/40 : relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: H04L 63/0861 : using biometrical features, e.g. fingerprint, retina-scan cryptographic mechanisms or cryptographic arrangements for entity authentication using biological data H04L9/3231

Abstract

A system, according to various embodiments, includes eyewear (or any other suitable wearable device) that includes one or more sensors (e.g., one or more accelerometers, magnetometers, position sensors, gyroscopes, and/or other suitable sensors) that may be used to monitor the gait of an individual (e.g., by monitoring the movement of the user'"'"'s head, or other portions of the user'"'"'s body, as the user ambulates). The system may further include one or more suitable computing devices for analyzing the individual'"'"'s gait. This information may then be used, for example, to: (1) identify one or more medical conditions associated with the individual; (2) assess the fit of a prosthetic device worn by the individual, and/or (3) assess an individual'"'"'s recovery from a particular injury or medical procedure.

Claims

1. A non-transitory computer-readable medium storing computer-executable instructions for:

receiving, at a gait server, a first set of information obtained over a first period of time defined by an individual'"'"'s routine movements from at least one sensor embedded into a pair of glasses worn by the individual and from at least one sensor positioned proximate at least one foot of the individual, wherein the first set of information corresponds to one or more movements of the individual'"'"'s head, to a height of the at least one foot from the ground, and to an orientation of the at least one foot relative to a central axis of the individual as the individual ambulates;

using, by the gait server, the first set of information to identify one or more relative peaks in linear acceleration of the head of the individual;

using, by the gait server, regression analysis techniques to identify a first slope of a first line corresponding to the one or more relative peaks in linear acceleration of the head of the individual over the first period of time to identify a first gait pattern of the individual;

receiving, at the gait server, a second set of information obtained over a second period of time defined by the individual'"'"'s routine movements from the at least one sensor embedded into the pair of glasses worn by the individual and from the at least one sensor positioned proximate to the at least one foot of the individual, wherein the second set of information corresponds to one or more movements of the individual'"'"'s head, to the height of the at least one foot from the ground, and to the orientation of the at least one foot relative to the central axis of the individual as the individual ambulates;

using, by the gait server, the second set of information to identify one or more relative peaks in linear acceleration of the head of the individual;

using, by the gait server, regression analysis techniques to identify a second slope of a second line corresponding to the one or more relative peaks in linear acceleration of the head of the individual over the second period of time to identify a second gait pattern of the individual;

comparing, by the gait server, the first gait pattern to the second gait pattern by comparing the first slope of the first line to the second slope of the second line;

in response to comparing the first gait pattern to the second gait pattern, identifying, by the gait server, the second gait pattern as an irregular gait pattern;

analyzing, by the gait server, the irregular gait pattern to determine whether the irregular gait pattern includes one or more particular gait patterns that are associated with a particular medical condition; and

in response to determining that the irregular gait pattern includes the one or more gait patterns, generating an alert that the individual may have the particular medical condition.

2. The non-transitory computer-readable medium of claim 1, wherein the at least one sensor is a gyroscope.

3. The non-transitory computer-readable medium of claim 1, wherein the step of analyzing the irregular gait pattern to determine whether the irregular gait pattern includes one or more particular gait patterns that are associated with a particular medical condition comprises:

comparing the one or more movements of the individual'"'"'s head, the height of the at least one foot from the ground, and the orientation of the at least one foot with one or more head movements, foot movements, and foot orientations that are associated with the particular medical condition; and

at least partially in response to determining that the one or more movements of the individual'"'"'s head, the height of the at least one foot from the ground, or the orientation of the at least one foot correspond to one or more head movements, foot heights, or foot orientations that are associated with the particular medical condition, determining that the irregular gait pattern includes one or more particular gait patterns that are associated with the particular medical condition.

4. The non-transitory computer-readable medium of claim 1, wherein the one or more gait patterns is foot drop.

5. The non-transitory computer-readable medium of claim 4, wherein the particular medical condition is a medical condition selected from a group consisting of:

stroke, amyotrophic lateral sclerosis, muscular dystrophy, Charcot Marie Tooth disease, multiple sclerosis, cerebral palsy, hereditary spastic paraplegia, and Friedrich'"'"'s ataxia.

6. The non-transitory computer-readable medium of claim 1, wherein the one or more gait patterns is propulsive gait.

7. The non-transitory computer-readable medium of claim 6, wherein the particular medical condition is a medical condition selected from a group consisting of:

carbon monoxide poisoning, manganese poisoning, and Parkinson'"'"'s disease.

8. The non-transitory computer-readable medium of claim 1, wherein the one or more gait patterns is waddling gait.

9. The non-transitory computer-readable medium of claim 8, wherein the particular medical condition is a medical condition selected from a group consisting of:

congenital hip dysplasia, muscular dystrophy, and spinal muscle atrophy.

10. The non-transitory computer-readable medium of claim 1, wherein the step of analyzing the irregular gait pattern to determine whether the irregular gait pattern includes one or more particular gait patterns that are associated with a particular medical condition comprises:

comparing the irregular gait pattern with the one or more gait patterns that are associated with the particular medical condition; and

in response to determining that the irregular gait pattern corresponds to the one or more gait patterns that are associated with the particular medical condition, determining that the irregular gait pattern includes one or more particular gait patterns that are associated with a particular medical condition.

11. The non-transitory computer-readable medium of claim 10, wherein the step of comparing the irregular gait pattern with the one or more gait patterns that are associated with the particular medical condition comprises comparing one or more relative peaks in linear acceleration associated with the irregular gait pattern of the individual with one or more relative peaks in linear acceleration of the one or more gait patterns that are associated with the particular medical condition.

12. A system for monitoring the gait of an individual, said system comprising:

a pair of glasses comprising one or more sensors for assessing the gait of the individual during a period of time corresponding to a recovery;

a computer system comprising a processor in communication with the one or more sensors of the pair of glasses, the processor being configured to;

obtain a first set of information over a first time after an injury or a medical procedure from the one or more sensors of the pair of glasses worn by the individual;

assess a first injury gait associated with the individual'"'"'s gait during the first time, wherein assessing the first injury gait comprises;

using the first set of information to identify one or more relative peaks in linear acceleration of a head of the individual, and

using regression analysis techniques to identify a first slope of a first line corresponding to the one or more relative peaks in linear acceleration of the head of the individual over the first period of time to identify the first injury gait pattern of the individual;

obtain a second set of information over a second time after the injury or the medical procedure from the one or more sensors of the pair of glasses worn by the individual;

assess a second injury gait associated with the individual'"'"'s gait during the second time, wherein the first time is closer to the injury or the medical procedure than the second time, wherein assessing the second injury gait comprises;

using the second set of information to identify one or more relative peaks in linear acceleration of the head of the individual, and

using regression analysis techniques to identify a second slope of a second line corresponding to the one or more relative peaks in linear acceleration of the head of the individual over the second period of time to identify the second injury gait pattern of the individual;

analyze the first injury gait and the second injury gait to determine whether the individual'"'"'s gait is normalizing to become more consistent with a previously established one or more movement patterns associated with the individual prior to the injury or the medical procedure;

in response to determining that the individual'"'"'s gait is not becoming more consistent with a previously established one or more movement patterns associated with the individual prior to the injury or the medical procedure, determine an alternate treatment plan for the individual; and

generate an alert to the individual comprising the alternate treatment plan.

13. A non-transitory computer-readable medium storing computer-executable instructions for:

receiving, at a gait server, a first set of information obtained over a first period of time defined by an individual'"'"'s routine movements from at least one sensor embedded into a pair of glasses worn by the individual, wherein the first set of information corresponds to one or more movements of the individual'"'"'s head as the individual ambulates;

using, by the gait server, the first set of information to identify one or more relative peaks in linear acceleration of the head of the individual;

using, by the gait server, regression analysis techniques to identify a first slope of a first line corresponding to the one or more relative peaks in linear acceleration of the head of the individual over the first period of time to identify a first gait pattern of the individual;

receiving, at the gait server, a second set of information obtained over a second period of time defined by the individual'"'"'s routine movements from the at least one sensor embedded into the pair of glasses worn by the individual, wherein the second set of information corresponds to one or more movements of the individual'"'"'s head as the individual ambulates;

using, by the gait server, the second set of information to identify one or more relative peaks in linear acceleration of the head of the individual;

using, by the gait server, regression analysis techniques to identify a second slope of a second line corresponding to the one or more relative peaks in linear acceleration of the head of the individual over the second period of time to identify a second gait pattern of the individual;

comparing, by the gait server, the first gait pattern to the second gait pattern by comparing the first slope of the first line to the second slope of the second line;

in response to comparing the first gait pattern to the second gait pattern, identifying, by the gait server, the second gait pattern as an irregular gait pattern;

analyzing, by the gait server, the irregular gait pattern to determine whether the irregular gait pattern includes one or more particular gait patterns that are associated with a particular medical condition; and

in response to determining that the irregular gait pattern includes the one or more gait patterns, generating an alert that the individual may have the particular medical condition.