User identification via motion and heartbeat waveform data

• US 9,693,711 B2
• Filed: 08/08/2016
• Issued: 07/04/2017
• Est. Priority Date: 08/07/2015
• Status: Active Grant

• Current Assignee: Fitbit, Inc. (Alphabet Inc.)
• Original Assignee: Fitbit, Inc. (Alphabet Inc.)
• Inventors: Ghoreyshi, Atiyeh, Wu, Anjian, Yuen, Shelten Gee Jao, Park, James
• Primary Examiner(s): Schwartz, Darren B
• Application Number:
  US15/231,627
• Publication Number:
  US 20170035327A1
• Time in Patent Office: 330 Days
• 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 5/332 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 identify a user of a wearable fitness monitor using data obtained using the wearable fitness monitor. Data obtained from motion sensors of the wearable fitness monitor and data obtained from heartbeat waveform sensors of the wearable fitness monitor may be used to identify the user.

Claims

1. A method, implemented on a wearable fitness monitor comprising one or more motion sensors, one or more heartbeat waveform sensors, and one or more processors, the method comprising:

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

(b) obtaining, by the one or more processors, a heartbeat waveform signature obtained using data from the one or more heartbeat waveform sensors, wherein the heartbeat waveform signature characterizes a detected heartbeat waveform of a wearer of the wearable fitness monitor;

(c) comparing, by the one or more processors, (i) the motion signature and the heartbeat waveform signature to (ii) one or more reference features of a reference user;

(d) determining, based on the comparison in (c) and by the one or more processors, that an identity of the wearer of the wearable fitness monitor is not the reference user; and

(e) responsive to determining that the identity of the wearer of the wearable fitness monitor is not the reference user, requiring the wearer to authenticate himself or herself, or discrediting a fitness metric obtained for the wearer via the wearable fitness monitor.

2. The method of claim 1, wherein the one or more motion sensors comprise an accelerometer, a gyroscope, a magnetometer, an altimeter, a Global Positioning System (GPS) receiver, or any combination thereof.

3. The method of claim 1, wherein the heartbeat waveform signature comprises a heartbeat waveform or one or more features derived therefrom.

4. The method of claim 3, wherein the one or more features derived from the heartbeat waveform comprise a slope of the heartbeat waveform, an area of the heartbeat waveform, a curvature of the heartbeat waveform, a polynomial fit of the heartbeat waveform, or any combination thereof.

5. The method of claim 3, wherein the one or more features derived from the heartbeat waveform comprise a frequency domain characteristic of the heartbeat waveform.

6. The method of claim 5, wherein the frequency domain characteristic of the heartbeat waveform is obtained from a Fourier transform or a cosine transform of the heartbeat waveform.

7. The method of claim 1, wherein the one or more heartbeat waveform sensors comprise a electrocardiography (ECG) sensor, a photoplethysmography (PPG) sensor, or any combination thereof.

8. The method of claim 7, wherein the one or more heartbeat waveform sensors comprise the PPG sensor and the ECG sensor.

9. The method of claim 8, wherein the heartbeat waveform signature is based on a difference between data obtained from the PPG sensor and the ECG sensor.

10. The method of claim 7, wherein the one or more heartbeat waveform sensors comprise a PPG sensor, the method further comprising:

obtaining a skin characteristic of the wearer using the PPG sensor.

11. The method of claim 10, wherein (d) comprises determining, based on the skin characteristic of the wearer and the comparison of (c), whether an identity of the wearer of the wearable fitness monitor is the reference user.

12. The method of claim 7, wherein the one or more heartbeat waveform sensors comprise a electrocardiography (ECG) sensor.

13. The method of claim 12, wherein the heartbeat waveform signature comprises a PQRST waveform or a feature derived therefrom.

14. The method of claim 13, wherein the feature derived from the PQRST waveform comprises a feature based on a PR interval, a PR segment, a QRS complex, a ST segment, a QT interval, or any combination thereof.

15. The method of claim 1, wherein (c) comprises applying a linear discriminant analysis, a neural network model, a support vector machine, a logistic regression analysis, a naive Bayes, a random forest, a decision tree, and any combination thereof to the motion signature and the heartbeat waveform signature.

16. The method of claim 1, the one or more reference features comprises a profile of a step by the reference user.

17. The method of claim 1, wherein (c) comprises applying a linear discriminant analysis classifier to the motion signature and the heartbeat waveform signature or a combination thereof.

18. The method of claim 1, wherein the movement experienced by the wearable fitness monitor is caused by a periodic motion 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.

19. The method of claim 1, further comprising, responsive to determining that the identity of the wearer of the wearable fitness monitor is not the reference user, preventing the wearable fitness monitor from allowing a transaction.

20. A system comprising:

(A) a wearable fitness monitor comprising;

one or more motion sensors,

one or more heartbeat waveform sensors, and

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

(B) 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) obtain a heartbeat waveform signature obtained using data from the one or more heartbeat waveform sensors, wherein the heartbeat waveform signature characterizes a detected heartbeat waveform of a wearer of the wearable fitness monitor,

(c) compare (i) the motion signature and the heartbeat waveform signature to (ii) one or more reference features of a reference user,

(d) based on the comparison in (c), determine that an identity of the wearer of the wearable fitness monitor is not the reference user; and

(e) responsive to determining that the identity of the wearer of the wearable fitness monitor is not the reference user, requiring the wearer to authenticate himself or herself, or discrediting a fitness metric obtained for the wearer via the wearable fitness monitor.

21. The system of claim 20, wherein the one or more motion sensors comprise an accelerometer, a gyroscope, a magnetometer, an altimeter, a GPS receiver, or any combination thereof.

22. The system of claim 20, wherein the heartbeat waveform signature comprises a heartbeat waveform or one or more features derived therefrom.

23. The system of claim 20, wherein the one or more heartbeat waveform sensors comprise a electrocardiography (ECG) sensor, a photoplethysmography (PPG) sensor, or any combination thereof.

24. The system of claim 20, wherein (c) comprises applying a linear discriminant analysis, a neural network model, a support vector machine, a logistic regression analysis, a naive Bayes, a random forest, a decision tree, and any combination thereof to the motion signature and the heartbeat waveform signature.

25. The system of claim 24, wherein the linear discriminant analysis, the neural network model, the support vector machine, the logistic regression analysis, the naive Bayes, the random forest, or the decision tree is trained using the one or more reference features of the reference user.

26. The system of claim 20, wherein the combination of the motion signature and the heartbeat waveform signature comprises a step count and a heart rate, and the one or more reference features of the reference user comprise a function between the reference user'"'"'s step count and the reference user'"'"'s heart rate.