# US 10,503,268 B2 · User identification via motion and heartbeat waveform data

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- Number: US 10,503,268 B2
- Document ID: US10503268B2
- Status: Granted
- Kind: B2
- Application number: 16/153,618
- Filed: 2018-10-05
- Published: 2019-02-14
- Granted: 2019-12-10
- Earliest claimed: 2015-08-07
- Assignees: Fitbit, Inc.
- Applicants: Fitbit, Inc.
- Inventors: Shelten Gee Jao Yuen; James Park; Atiyeh Ghoreyshi; Anjian Wu
- Web page: https://patentgrep.com/patent/US10503268B2

## CPC

- A63B 69/36: for golf
- A61B 5/0002: {Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network (endoradiosondes A61B5/07)}
- A61B 5/02416: {using photoplethysmograph signals, e.g. generated by infrared radiation (A61B5/14552 takes precedence)}
- A61B 5/02438: {with portable devices, e.g. worn by the patient}
- A61B 5/11: Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb {(A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721)}
- A61B 5/1112: {Global tracking of patients, e.g. by using GPS}
- A61B 5/1118: {Determining activity level}
- A61B 5/112: {Gait analysis}
- A61B 5/1123: {Discriminating type of movement, e.g. walking or running (A61B5/1116, A61B5/112 take precedence)}
- 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)
- A61B 5/441: {Skin evaluation, e.g. for skin disorder diagnosis}
- A61B 5/6802: {Sensor mounted on worn items}
- A61B 5/681: {Wristwatch-type devices}
- A61B 5/725: {using specific filters therefor, e.g. Kalman or adaptive filters (specific diagnostics methods using using bioelectric or biomagnetic signals A61B5/316)}
- A61B 5/7257: {using Fourier transforms}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- A61B 5/7267: {involving training the classification device}
- A61B 5/7271: {Specific aspects of physiological measurement analysis (specific diagnostics methods using bioelectric or biomagnetic signals A61B5/316)}
- A61B 5/742: {using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339)}
- A61B 5/7475: {User input or interface means, e.g. keyboard, pointing device, joystick}
- 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}
- G01C 22/006: {Pedometers}
- G01P 1/02: Housings
- G01P 13/00: Indicating or recording presence, absence, or direction, of movement (electric switches H01H; counting moving objects G06M7/00)
- G01S 19/00: Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S 19/19: Sporting applications
- G06F 1/163: {Wearable computers, e.g. on a belt}
- 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)}
- G06F 21/32: using biometric data, e.g. fingerprints, iris scans or voiceprints
- G06Q 10/40: Business processes related to social networking or social networking services
- G06V 40/10: Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V 40/23: {Recognition of whole body movements, e.g. for sport training}
- G09B 5/02: with visual presentation of the material to be studied, e.g. using film strip
- A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet}
- A61B 5/0024: {for multiple sensor units attached to the patient, e.g. using a body or personal area network}
- A61B 5/1032: {Determining colour of tissue for diagnostic purposes}
- A61B 5/1172: using fingerprinting
- A61B 5/332: Portable devices specially adapted therefor
- A61B 5/6844: {Monitoring or controlling distance between sensor and tissue}
- A61B 2560/0223: of calibration, e.g. protocols for calibrating sensors
- A61B 2562/0219: Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
- G06F 2218/10: by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
- G06V 40/15: {Biometric patterns based on physiological signals, e.g. heartbeat, blood flow}
- G06V 40/20: Movements or behaviour, e.g. gesture recognition (recognition of facial expressions G06V40/16)
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- A63B: APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT (apparatus for passive exercising, massage A61H)
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)
- G01P: MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT (measuring or recording blood flow A61B5/02, A61B8/06; monitoring speed or deceleration of electrically-propelled vehicles B60L3/00; vehicle lighting systems adapted to indicate speed B60Q1/54; determining position or course in navigation, measuring ground distance in geodesy or surveying G01C; combined measuring devices for measuring two or more variables of movement G01C23/00; measuring velocity of sound G01H; measuring velocity of light G01J7/00; measuring direction or velocity of solid objects by reception or emission of radiowaves or other waves and based on propagation effects, e.g. Doppler effect, propagation time, direction of propagation, G01S; measuring speed of nuclear radiation G01T; measuring acceleration of gravity G01V; {measuring or recording the speed of trains B61L23/00; speed indicators incorporated in motor vehicles B60K35/00; measuring frequency or phase G01R; traffic control G08G})
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## 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

### Claim 1

A method comprising:
receiving motion data generated by one or more motion sensors of a wearable device when the wearable device is worn by a wearer;
receiving an indication that the wearable device is being used to authenticate a transaction;
determining a first motion signature based at least on the motion data, wherein the first motion signature characterizes a movement experienced by the wearable device;
comparing the first motion signature to one or more reference features of a reference user;
determining, based on the comparison, that the wearer of the wearable device is not the reference user; and
preventing, responsive to determining that the wearer of the wearable device is not the reference user, the wearable device from authenticating the transaction.

### Claim 2 (depends on 1)

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

### Claim 3 (depends on 1)

The method of claim 1, wherein comparing the first motion signature to one or more reference features of the reference user comprises:
generating a first test feature vector based at least on the first motion signature;
applying a machine learning classifier to the first test feature vector, wherein
the machine learning classifier was trained using motion data corresponding to one or more reference features of the reference user,
the machine learning classifier is configured to receive feature vectors as inputs and provide classifications of the feature vectors as outputs, and
the classifications indicate whether the feature vectors belong to the reference user; and
determining that the first test feature vector is not associated with the reference user, based at least on a classification generated by the machine learning classifier.

### Claim 4 (depends on 3)

The method of claim 3, wherein the machine learning classifier comprises a linear discriminant classifier, a neural network model, a support vector machine, a logistic regression model, a naive Bayes, a random forest, a decision tree, and any combination thereof to the motion signature and the heartbeat waveform signature.

### Claim 5 (depends on 1)

The method of claim 1, further comprising: obtaining a second motion signature using the motion data, and comparing the second motion signature, as well as the first motion signature, to one or more reference features of a reference user.

### Claim 6 (depends on 1)

The method of claim 1, the first motion signature comprises a profile of a step.

### Claim 7 (depends on 1)

The method of claim 1, wherein the movement experienced by the wearable device 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.

### Claim 8 (depends on 1)

The method of claim 1, further comprising, responsive to determining that the wearer of the wearable device is not the reference user, discrediting a fitness metric obtained for the reference user via the wearable device.

### Claim 9 (depends on 1)

The method of claim 1, wherein the determining the first motion signature comprises low-pass filtering the motion data.

### Claim 10 (depends on 1)

The method of claim 1, wherein the first motion signature characterizes a cycle of periodic motion of the movement experienced by the wearable device.

### Claim 11 (depends on 1)

The method of claim 1, wherein the motion data obtained from the reference user comprise a characteristic of a periodic motion.

### Claim 12 (depends on 1)

The method of claim 1, wherein the first motion signature is a time domain representation of a periodic motion of the movement experienced by the wearable device.

### Claim 13 (depends on 1)

The method of claim 1, wherein the first motion signature comprises a frequency domain representation of a periodic motion of the movement experienced by the wearable device.

### Claim 14 (depends on 13)

The method of claim 13, wherein the frequency domain representation of the periodic motion of the movement experienced by the wearable device comprises a spectral component in the periodic motion.

### Claim 15 (depends on 1)

The method of claim 1, wherein determining that the wearer of the wearable device is not the reference user comprises determining that the first motion signature is an invalid motion for a human wearer.

### Claim 16 (depends on 1)

The method of claim 1, wherein the determining the first motion signature comprises low-pass filtering the motion data of the wearer.

### Claim 17

A wearable device comprising:
(A) one or more motion sensors; and
(B) one or more processors configured to:
provide motion data generated by the one or more motion sensors when the wearable device is worn by a wearer;
obtain an indication that the wearer of the wearable device is not a reference user, the indication being based on a comparison between a first motion signature obtained from the motion data and one or more reference features of the reference user; and
preventing, responsive to obtaining the indication that the wearer is not the reference user, the wearable device from authenticating a transaction.

### Claim 18 (depends on 17)

The device of claim 17, wherein the one or more processors are configured to compare between the first motion signature obtained from the motion data and the one or more reference features of the reference user to obtain the indication that the wearer of the wearable device is not the reference user.

### Claim 19

A method comprising:
receiving first motion data generated by one or more motion sensors of a wearable device worn by a wearer;
determining a first motion signature based at least on the first motion data, wherein the first motion signature characterizes a first movement experienced by the wearable device;
comparing the first motion signature to one or more reference features of a reference user;
determining, based on the comparison, that the wearer of the wearable device is the reference user;
receiving second motion data generated by the one or more motion sensors of the wearable device;
determining a second motion signature based at least on the second motion data, wherein the second motion signature characterizes a second movement experienced by the wearable device;
determining the second motion signature corresponds to an invalid motion feature, the invalid motion feature characterizing a non-human movement; and
discrediting, responsive to determining that (i) the wearer of the wearable device is the reference user and (ii) the second motion signature corresponds to an invalid motion feature, a fitness metric obtained for the reference user via the wearable device.

### Claim 20 (depends on 19)

The method of claim 19, wherein the non-human movement comprises a vehicle movement.

### Claim 21 (depends on 19)

The method of claim 19, wherein the fitness metric characterizes a periodic movement.

### Claim 22 (depends on 21)

The method of claim 21, wherein the periodic movement 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, and riding an animal.

### Claim 23

A wearable device comprising:
(A) one or more motion sensors; and
(B) one or more processors configured to:
provide first motion data and second motion data generated by the one or more motion sensors when the wearable device is worn by a reference user;
obtain a first indication that a wearer of the wearable device is the reference user, the indication being based on a comparison between a first motion signature obtained from the first motion data and one or more reference features of the reference user;
obtain a second indication that a second motion signature corresponds to an invalid motion feature, wherein the second motion signature is obtained from the second motion data, and wherein the invalid motion feature characterizes a non-human movement; and
discredit, responsive to obtaining the first indication and the second indication, a fitness metric obtained for the reference user via the wearable device.

### Claim 24 (depends on 23)

The device of claim 23, wherein the non-human movement comprises a vehicle movement.

### Claim 25 (depends on 23)

The device of claim 23, wherein the fitness metric characterizes a periodic movement.

### Claim 26 (depends on 25)

The device of claim 25, wherein the periodic movement 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, and riding an animal.

### Claim 27 (depends on 25)

The device of claim 25, wherein the invalid motion feature has a periodicity within a threshold periodicity for a given time period.

### Claim 28 (depends on 25)

The device of claim 25, wherein the invalid motion feature comprises one or more periodic motion contributions from one or more spatial dimensions that is less than a threshold.

### Claim 29 (depends on 25)

The device of claim 25, wherein the invalid motion feature is a periodic motion having a cycle-to-cycle consistency greater than a threshold.

### Claim 30 (depends on 25)

The device of claim 25, wherein the first or second indication is obtained using classification techniques selected from Linear Discriminant Analysis, neural network, clustering, logistic regression, support vector machine, naïve Bayes, and any combinations thereof.

### Claim 31 (depends on 25)

The device of claim 25, wherein the second indication is obtained by:
obtaining an additional signature using data from one or more additional sensors, and
determining that the second motion signature, the additional signature, or a combination thereof is inconsistent with at least one human activity.

### Claim 32 (depends on 31)

The device of claim 31, wherein the additional signature is a heart rate or a heartbeat waveform.

### Claim 33 (depends on 25)

The device of claim 25, wherein the second indication is obtained by determining whether the first and second motion signatures represent a same characteristic of a periodic motion.