USER IDENTIFICATION VIA DATA COLLECTED FROM SENSORS OF A WEARABLE FITNESS MONITOR

• US 20170035328A1
• Filed: 08/08/2016
• Published: 02/09/2017
• Est. Priority Date: 08/07/2015
• Status: Abandoned Application

• Current Assignee: Fitbit, Inc. (Alphabet Inc.)
• Original Assignee: Fitbit, Inc. (Alphabet Inc.)
• Inventors: Yuen, Shelten Gee Jao, Park, James, Ghoreyshi, Atiyeh
• Application Number:
  US15/231,636
• Publication Number:
  US 20170035328A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• 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 5/0024 for multiple sensor units attached to the patient, e.g. using a body or personal area network
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/1032 Determining colour for diagnostic purposes measuring colour in general G01J3/46
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take 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 5/1172 using fingerprinting
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/332 Portable devices specially adapted therefor
• CPC Class Codes: A61B 5/441 Skin evaluation, e.g. for skin disorder diagnosis
• CPC Class Codes: A61B 5/6802 Sensor mounted on worn items
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6844 : Monitoring or controlling distance between sensor and tissue
• CPC Class Codes: A61B 5/725 : using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7257 : using Fourier transforms
• CPC Class Codes: A61B 5/7264 : Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 : involving training the classification device
• CPC Class Codes: A61B 5/7271 : Specific aspects of physiological measurement analysis A61B5/316, A61B5/349 and A61B5/374 take precedence
• CPC Class Codes: A61B 5/742 : using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7475 : User input or interface means, e.g. keyboard, pointing device, joystick
• 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: A63B 69/36 : for golf
• CPC Class Codes: G01C 22/006 : Pedometers
• CPC Class Codes: G01P 1/02 : Housings
• CPC Class Codes: G01P 13/00 : Indicating or recording presence, absence, or direction, of movement electric switches H01H; counting moving objects G06M7/00
• CPC Class Codes: G01S 19/00 : Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
• CPC Class Codes: G01S 19/19 : Sporting applications
• CPC Class Codes: G06F 1/163 : Wearable computers, e.g. on a belt
• CPC Class Codes: G06F 21/32 : using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06F 2218/10 : by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
• CPC Class Codes: G06F 3/017 : Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06Q 50/01 : Social networking
• CPC Class Codes: G06V 40/10 : Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
• CPC Class Codes: G06V 40/15 : Biometric patterns based on physiological signals, e.g. heartbeat, blood flow
• CPC Class Codes: G06V 40/20 : Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• CPC Class Codes: G06V 40/23 : Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: G09B 5/02 : with visual presentation of the material to be studied, e.g. using film strip
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients

Abstract

The disclosure relates to methods, devices, and systems to recognize a user of a wearable fitness monitor using information obtained using the wearable fitness monitor. Motion data obtained from motion sensors of the wearable fitness monitor may be used to recognize or authenticate the user.

Claims

1. A method comprising:

(a) obtaining a motion signature obtained using data from one or more motion sensors of a wearable fitness monitor configured to be worn by a person, wherein the motion signature characterizes a movement experienced by the wearable fitness monitor;

(b) comparing the motion signature to a reference motion feature for a user; and

(c) based on the comparison in (b), determining whether an identity of a wearer of the fitness monitor is the user.

2. The method of claim 1, wherein the motion signature characterizes a cycle of periodic movement of the person wearing the wearable fitness monitor.

3. The method of claim 2, wherein the reference motion feature is a reference cycle for periodic movement of a user.

6. The method of claim 2, wherein the user'"'"'s periodic motion is selected from the group consisting of running, walking, cycling, swimming, weight lifting, climbing, rowing, a gymnastic exercise, dancing, an aerobic exercise, a yoga routine, golfing, swinging a club, swinging a racquet, striking a ball or another object, swimming, diving, surfing, skating, skiing, skate boarding, exercising on a machine, driving a vehicle, and riding an animal.

7. The method of claim 1, wherein the reference motion feature is a characteristic of a periodic motion.

9. The method of claim 1, wherein the obtaining the motion signature obtained using the data from the one or more motion sensors comprises low-pass filtering the data from the one or more motion sensors.

10. The method of claim 9, wherein the obtaining the motion signature obtained using the data from the one or more motion sensors further comprises obtaining a cycle profile from the low-pass filtered data.

11. The method of claim 10, where the obtaining the cycle profile from the low-pass filtered data comprises:

obtaining local minima from the low-passed filtered data;

dividing the low-passed filtered data into two or more segments using the local minima; and

obtaining the cycle profile from the two or more segments.

15. The method of claim 9, wherein the obtaining the motion signature obtained using the data from the one or more motion sensors further comprises:

extracting one or more features from the cycle profile or values derived from the cycle profile.

16. The method of claim 15, wherein each feature is selected from the group consisting of:

a slope, an inflection, a zero crossing, a derivative, a moment, a cumulant, and any combination thereof.

17. The method of claim 15, wherein comparing the motion signature to the reference motion feature for the user comprises:

obtaining a classifier using motion data obtained from the user; and

applying the classifier to the extracted one or more features,

wherein the classifier takes the one or more features as inputs and provides a classification of the wearer being the user or not the user as an output.

18. The method of claim 17, wherein the classifier comprises a linear discriminant analysis classifier.

19. The method of claim 17, wherein the classifier comprises a neural network classifier.

20. The method of claim 17, wherein the classifier is trained using at least one cycle profile derived from motion data obtained from the user.

21. The method of claim 17, further comprising, updating the classifier using additional motion data obtained from the user.

4-5. -5. (canceled)

8. (canceled)

12-14. -14. (canceled)

22. A system comprising:

a wearable fitness monitor configured to be worn by a person and comprising;

one or more first motion sensors, and

a communication interface configured for communicating data from the one or more first motion sensors to a device external to the wearable fitness monitor; and

classification logic configured to;

(a) obtain a motion signature obtained using data from the one or more motion sensors, wherein the motion signature characterizes a movement experienced by the wearable fitness monitor,

(b) compare the motion signature to a reference motion feature for a user, and

(c) based on the comparison in (b), determine whether an identity of a wearer of the fitness monitor is the user.

23. The system of claim 22, wherein the motion signature characterizes a cycle of periodic movement of the person wearing the wearable fitness monitor.

24. The system of claim 23, wherein the reference motion feature is a reference cycle for periodic movement of a user.

27. The system of claim 23, wherein the user'"'"'s periodic motion is selected from the group consisting of running, walking, cycling, swimming, weight lifting, climbing, rowing, a gymnastic exercise, dancing, an aerobic exercise, a yoga routine, golfing, swinging a club, swinging a racquet, striking a ball or another object, swimming, diving, surfing, skating, skiing, skate boarding, exercising on a machine, driving a vehicle, and riding an animal.

28. The system of claim 22, wherein the reference motion feature is a characteristic of a periodic motion.

30. The system of claim 22, wherein (a) comprises low-pass filtering the data from the one or more motion sensors.

31. The system of claim 30, wherein (a) further comprises obtaining a cycle profile from the low-pass filtered data.

32. The system of claim 31, where the obtaining the cycle profile from the low-pass filtered data comprises:

obtaining local minima from the low-passed filtered data;

dividing the low-passed filtered data into two or more segments using the local minima; and

obtaining the cycle profile from the two or more segments.

36. The system of claim 30, wherein the obtaining the motion signature obtained using the data from the one or more motion sensors further comprises:

extracting one or more features from the cycle profile or values derived from the cycle profile.

37. The system of claim 36, wherein each feature is selected from the group consisting of:

a slope, an inflection, a zero crossing, a derivative, a moment, a cumulant, and any combination thereof.

38. The system of claim 36, wherein comparing the motion signature to the reference motion feature for the user comprises:

obtaining a classifier using motion data obtained from the user; and

applying the classifier to the extracted one or more features,

wherein the classifier takes the one or more features as inputs and provides a classification of the wearer being the user or not the user as an output.

39. The system of claim 38, wherein the classifier comprises a linear discriminant analysis classifier.

40. The system of claim 38, wherein the classifier comprises a neural network classifier.

41. The system of claim 38, wherein the classifier is trained using at least one cycle profile derived from motion data obtained from the user.

42. The system of claim 38, further comprising, updating the classifier using additional motion data obtained from the user.

25-26. -26. (canceled)

29. (canceled)

33-35. -35. (canceled)