SYSTEM FOR MONITORING INDIVIDUALS AS THEY AGE IN PLACE

• US 20160071390A1
• Filed: 12/05/2014
• Published: 03/10/2016
• 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, Richard Chester Jr., Workman, Matthew Allen, Murphy, Meghan Kathleen, Steen, Matthew David
• Granted Patent: US 9,795,324 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 computer-implemented method, and related system, for monitoring the wellbeing of an individual by providing eyewear that includes at least one sensor for monitoring the motion of the user. In various embodiments, the system receives data generated by the at least one sensor, uses the data to determine the user'"'"'s movements using the received data, and compares the user'"'"'s movements to previously established movement patterns of the user. If the system detects one or more inconsistencies between the user'"'"'s current movements as compared to the previously established movement patterns of the user, the system may notify the user or a third party of the detected one or more inconsistencies. The system may similarly monitor a user'"'"'s compliance with a medical regime and notify the user or a third party of the user'"'"'s compliance with the regime.

Claims

1. A computer-implemented method of monitoring the wellbeing of an individual, the method comprising the steps of:

a. providing a user with computerized eyewear comprising at least one sensor for monitoring the motion of the user;

b. receiving, by a processor, data generated by the at least one sensor;

c. at least partially in response to receiving data generated by the at least one sensor, determining, by a processor, the user'"'"'s movements using the received data;

d. at least partially in response to determining the user'"'"'s movements, comparing, by a processor, the user'"'"'s movements to previously established one or more movement patterns for the user;

e. detecting, by a processor, one or more inconsistencies between the current user'"'"'s movements as compared to the previously established one or more movement patterns;

f. at least partially in response to detecting the one more inconsistencies, notifying, by a processor, at least one recipient selected from a group consisting of;

the user or a third party of the detected one or more inconsistencies.

2. The computer-implemented method of claims 1, wherein the at least one sensor comprises at least one sensor selected from a group consisting of:

a. a motion sensor;

b. an accelerometer;

c. a gyroscope;

d. a geomagnetic sensor;

e. a global positioning system sensor;

f. an impact sensor;

g. a microphone;

h. a forward facing camera;

i. a heart rate monitor;

j. a pulse oximeter;

k. a blood alcohol monitor;

l. a respiratory rate sensor; and

m. a transdermal sensor.

3. The computer-implemented method of claim 2, wherein the at least one sensor comprises at least one sensor selected from a group consisting of:

a motion sensor, an accelerometer, a global positioning sensor, a gyroscope, and a forward facing camera.

4. The computer-implemented method of claim 2, wherein the method further comprises the step of:

a. calculating, by a processor, a number of steps taken by the user in a particular day;

b. at least partially in response to calculating the number of steps, comparing, by a processor, the calculated number of steps taken by the user in the particular day to a predetermined average number of steps taken by the user in a day; and

c. at least partially in response to comparing the calculated number of steps to the predetermined average number of steps, notifying the user or a third party if the calculated number of steps in the particular day is less than a predetermined percentage of the predetermined average number of steps taken by the user in a day.

5. The computer-implemented method of claim 2, further comprising the steps of:

a. detecting, by a processor, whether the user moves during a predefined time period; and

b. at least partially in response to detecting whether the user moves during the predefined time period, notifying, by a processor, the at least one recipient selected from a group consisting of;

the user or a third party if the user does not move during the predefined time period.

6. The computer-implemented method of claim 2, further comprising the steps of:

a. detecting, by a processor, from the received data generated by the at least one sensor if the user experiences a sudden acceleration or sudden impact; and

b. at least partially in response to detecting that the user has experienced a;

sudden acceleration or sudden impact, notifying, by a processor, the user or a third party that the user experienced the sudden acceleration or sudden impact.

7. The computer-implemented method of claim 2, further comprising the steps of:

a. detecting, by a processor, from the received data generated by the at least one sensor;

(1) whether the user is breathing; and

(2) whether the user'"'"'s heart is beating; and

b. at least partially in response to determining that the user is not breathing or that the user'"'"'s heart is not beating, sending a notification to a third party.

8. The computer-implemented method of claim 2, further comprising the steps of:

a. receiving, by a processor, from the user or third party, a medicine regime associated with the user;

b. storing, by a processor, the medicine regime in memory;

c. receiving, by a processor, data generated by a forward facing camera associated with the computerized eyewear;

d. analyzing, by a processor, the received data to determine data selected from a group consisting of one or more;

i. types of medicine taken by the user;

ii. times the medicine is taken by the user; and

iii. doses of the medicine taken by the user;

e. at least partially in response to analyzing the received data, comparing, by a processor, the one or more of the types of medicine taken, the one or more times the medicine is taken, or the one or more doses of medicine taken to the stored medicine regime for the user;

f. at least partially in response to comparing the one or more of the type of medicine taken, the time the medicine is taken and the dose of medicine taken, identifying, by a processor, one or more inconsistencies between the stored medicine regime, and the one or more types of medicine taken, the one or more times the medicine is taken, or the one or more doses of medicine taken;

g. at least partially in response to identifying the one or more inconsistencies between the medicine regime and the one or more of the types of medicine taken, the one or more times the medicine is taken, or the one or more doses of medicine taken, sending an alert to the user or a third party of the one or more inconsistencies.

9. The computer-implemented method of claim 8, wherein:

a. the data generated comprises one or more images captured by the forward facing camera;

b. the step of analyzing the received data further comprises;

i. detecting, by a processor, one or more pills in the one or more images;

ii. comparing, by a processor, the one or more detected pills found in the one or more images to one or more known images of pills stored in a database;

iii. identifying, by a processor, the one or more pills by matching the one or more pills from the one or more images to the one or more known images of pills stored in the database; and

iv. detecting, by a processor, a time that the one or more images were taken.

10. A computer-implemented method of monitoring the wellbeing of an individual, the method comprising the steps of:

a. providing a user with a computerized wearable comprising at least one sensor for monitoring actions taken by a user;

b. receiving, by a processor, a medicine regime associated with the user;

c. receiving, by a processor, data generated by the at least one sensor;

d. analyzing, by a processor, the received data generated by the at least one sensor to determine data selected from the group consisting of one or more;

i. types of medicine taken by the user;

ii. times the medicine is taken by the user; and

iii. doses of medicine taken by the user;

e. at least partially in response to analyzing the received data generated by the at least one sensor, comparing, by a processor, the medicine regime for the user to the determined one or more of the types of medicine taken by the user, the one or more times the medicine is taken by the user, or the one or more doses of medicine taken by the user;

f. detecting, by a processor, one or more inconsistencies between the medicine regime associated with the user and the determined one or more of the types of medicine taken by the user, the one or more times the medicine is taken by the user, or the one or more doses of medicine taken by the user;

g. at least partially in response to detecting one or more inconsistencies, notifying, by a processor, at least one recipient selected from a group consisting of;

the user or the third party of the detected inconsistencies.

11. The computer-implemented method of claims 10, wherein the at least one sensor further comprises one or more sensors selected from a group consisting of:

a camera, a scanner, a motion sensor, and a microphone.

12. The computer-implemented method of claim 10, wherein the computerized wearable is eyewear.

13. The computer-implemented method of claim 10, wherein:

a. the data generated is one or more images captured by a forward facing camera;

b. the step of analyzing the received data further comprises;

i. detecting, by a processor, one or more pills in the one or more images;

ii. comparing, by a processor, the one or more detected pills found in the one or more images to one or more known images of pills stored in a database;

iii. identifying, by a processor, the one or more pills by matching the one or more pills from the one or more images to the one or more known images of pills stored in the database; and

iv. detecting, by a processor, a time that the one or more images were taken.

14. The computer-implemented method of claim 10, further comprising the step of:

a. detecting, by a processor, a level of one or more medicines in the user'"'"'s bloodstream;

b. comparing, by a processor, the level of the one or medicines in the user'"'"'s bloodstream to a predefined level for each of the one or more medicines stored in a database for the user; and

c. at least partially in response to comparing the level of the one or medicines in the user'"'"'s bloodstream, notifying the at least one recipient selected from a group consisting of;

the user or a third party when the level of the one or more medicines is below the predefined level for each of the one or more medicines.

15. The computer-implemented method of claim 10, further comprising the steps of:

a. determining, by a processor, the user'"'"'s one or more movements using the received data generated by the at least one sensor;

b. analyzing, by a processor, the received data generated by the at least one sensor to determine the one or more movements associated with the user;

c. at least partially in response to analyzing the received data, comparing, by a processor, the user'"'"'s one or more movements to previously established one or more movement patterns for the user;

d. detecting, by a processor, one or more inconsistencies between the user'"'"'s one or more movements as compared to the previously established one or more movement patterns; and

e. at least partially in response to detecting one or more inconsistencies between the current user'"'"'s one or more movements as compared to previously established one or more movement patterns, notifying, by a processor, at least one recipient selected from a group consisting of;

the user or a third party of the detected one or more inconsistencies.

16. Eyewear for monitoring the well-being of an individual, said eyewear comprising:

a. a frame portion that is adapted for supporting at least one lens, a front portion of the frame portion defining a first lateral side and a second lateral side;

b. a first temple attached adjacent the first lateral side, the first temple being mounted to extend, at least selectively, rearwardly away from the frame portion so that a first temple distal portion may engage a first ear of a wearer of the eyewear to at least partially support the eyewear while the eyewear is being worn adjacent the wearer'"'"'s face;

c. a second temple attached adjacent the second lateral side, the second temple being mounted to extend, at least selectively, rearwardly away from the frame portion so that a second temple distal portion may engage a second ear of the wearer to at least partially support the eyewear while the eyewear is being worn adjacent the wearer'"'"'s face;

d. at least one well-being monitoring device that is adapted to monitor the well-being of the wearer of the eyewear, wherein;

i. the at least one well-being monitoring device is adapted for tracking the movement of the wearer; and

ii. the eyewear is adapted to transmit data regarding the movement of the wearer for use in determining whether the wearer'"'"'s movement is inconsistent with one or more previously-established movement patterns of the individual.

17. The eyewear of claim 16, wherein the well-being monitoring device comprises one or more sensors selected from a group consisting of:

a. motion sensors;

b. accelerometers;

c. gyroscopes;

d. geomagnetic sensors;

e. global positioning system sensors;

f. impact sensors;

g. microphones;

h. forward facing cameras;

i. heart rate monitors;

j. pulse oximeters;

k. blood alcohol monitors;

l. respiratory rate sensors; and

m. transdermal sensors.

18. The eyewear of claim 16, wherein the one or more sensors are adapted for tracking the movement of the wearer while the wearer is wearing the eyewear.

19. The eyewear of claim 16, wherein the one or more movement patterns comprise one or more patterns of physical movement.

20. The eyewear of claim 16, wherein the one or more movement patterns comprise one or more patterns of movement between one or more rooms within a particular dwelling.