# US 9,396,643 B2 · Biometric authentication > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,396,643 B2 - Document ID: US9396643B2 - Status: Granted - Kind: B2 - Application number: 14/522,230 - Filed: 2014-10-23 - Published: 2015-04-23 - Granted: 2016-07-19 - Earliest claimed: 2013-10-23 - Assignees: Quanttus, Inc. - Applicants: Quanttus, Inc. - Inventors: David Da He; Richard Robehr Bijjani; Shahid Azim; Bogart Vargas - Web page: https://patentgrep.com/patent/US9396643B2 ## CPC - G08B 21/0453: {worn on the body to detect health condition by physiological monitoring, e.g. electrocardiogram, temperature, breathing (detecting, measuring or recording for diagnostic purposes A61B5/00)} - A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet} - A61B 5/02028: {Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction} - A61B 5/0205: Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition - A61B 5/02055: {Simultaneously evaluating both cardiovascular condition and temperature} - A61B 5/02108: {from analysis of pulse wave characteristics} - A61B 5/02125: {of pulse wave propagation time} - A61B 5/02416: {using photoplethysmograph signals, e.g. generated by infrared radiation (A61B5/14552 takes precedence)} - A61B 5/02427: {Details of sensor} - A61B 5/0285: Measuring or recording phase velocity of blood waves - A61B 5/029: Measuring blood output from the heart, e.g. minute volume - A61B 5/08: Measuring devices for evaluating the respiratory organs - 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 5/1102: {Ballistocardiography} - A61B 5/1118: {Determining activity level} - A61B 5/113: occurring during breathing - 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 5/14542: {for measuring blood gases (A61B5/14551 takes precedence)} - A61B 5/165: {Evaluating the state of mind, e.g. depression, anxiety} - A61B 5/4809: {Sleep detection, i.e. determining whether a subject is asleep or not} - A61B 5/4815: {Sleep quality} - A61B 5/4818: {Sleep apnoea} - A61B 5/4833: {Assessment of subject's compliance to treatment} - A61B 5/486: {Biofeedback (using electroencephalography [EEG] A61B5/375)} - A61B 5/6801: {specially adapted to be attached to or worn on the body surface} - A61B 5/681: {Wristwatch-type devices} - A61B 5/6824: {Arm or wrist} - A61B 5/6898: {Portable consumer electronic devices, e.g. music players, telephones, tablet computers} - A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor} - A61B 5/7278: {Artificial waveform generation or derivation, e.g. synthesizing signals from measured signals} - A61B 5/7282: {Event detection, e.g. detecting unique waveforms indicative of a medical condition (cough events A61B5/0823; seizures A61B5/4094; sleep apnoea A61B5/4818)} - A61B 5/742: {using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339)} - A61B 5/746: {Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms} - G06F 3/0481: based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance - G06F 21/32: using biometric data, e.g. fingerprints, iris scans or voiceprints - G08B 21/06: indicating a condition of sleep, e.g. anti-dozing alarms - A61B 5/02007: {Evaluating blood vessel condition, e.g. elasticity, compliance} - A61B 5/02405: {Determining heart rate variability} - A61B 5/02438: {with portable devices, e.g. worn by the patient} - A61B 5/0531: Measuring skin impedance - A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)} - A61B 5/363: Detecting tachycardia or bradycardia - H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks - G08B: SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract The technology described in this document is embodied in a method that includes processing data in a dataset that represents time-varying information about at least one pulse pressure wave propagating through blood in a subject acquired at a location of the subject. The method also includes determining whether one or more segments of the dataset were captured from a subject other than an expected subject by analyzing morphological features of the segments. ## Claims ### Claim 1 A method comprising: processing data in a first dataset that represents time-varying information about at least one pulse pressure wave propagating through blood in a subject acquired at a location of the subject; processing data in a second dataset that represents time-varying information about motion of the subject acquired at the location of the subject; and determining whether one or more segments of the datasets were captured from a subject other than an expected subject by analyzing morphological features of the segments, wherein the information about at least one pulse pressure wave propagating through blood in the subject comprises photoplethysmographic (PPG) data and the information about motion of the subject comprises one or both of motioncardiogram (MoCG) data and gross motion data. ### Claim 2 (depends on 1) The method of claim 1, wherein the data is acquired continuously. ### Claim 3 (depends on 1) The method of claim 1, wherein the data is acquired at a frequency of at least 16 Hz. ### Claim 4 (depends on 1) The method of claim 1, wherein the data is acquired at a frequency of between 75 Hz and 85 Hz. ### Claim 5 (depends on 1) The method of claim 1, wherein the data is acquired at a single location of the subject. ### Claim 6 (depends on 5) The method of claim 5, wherein the data is acquired by a device worn by the subject. ### Claim 7 (depends on 6) The method of claim 6, wherein the device is mobile and does not reduce a mobility of the subject. ### Claim 8 (depends on 6) The method of claim 6, wherein the device processes the data. ### Claim 9 (depends on 5) The method of claim 5, wherein the single location is an arm of the subject. ### Claim 10 (depends on 9) The method of claim 9, wherein the single location is a wrist of the subject. ### Claim 11 (depends on 1) The method of claim 1, wherein the determining comprises analyzing other biometric data including one or more of electrical skin impedance, respiratory rate, heart rate, heart rate variability, PPG morphology, and vocal sound frequency of the subject. ### Claim 12 (depends on 11) The method of claim 11, wherein analyzing the other biometric data comprises determining whether the subject is under distress. ### Claim 13 (depends on 1) The method of claim 1, wherein the determining comprises analyzing confidential information provided by the subject. ### Claim 14 (depends on 13) The method of claim 13, wherein the confidential information includes one or more of a password, a personal identification number, and a predefined gesture. ### Claim 15 (depends on 1) The method of claim 1, wherein the analyzing comprises comparing morphological features of different segments of biometric data. ### Claim 16 (depends on 1) The method of claim 1, further comprising taking an action when it is determined that one or more of the segments were captured from a subject other than the expected subject. ### Claim 17 (depends on 16) The method of claim 16, wherein taking an action comprises prompting the subject to provide confidential information to authenticate the subject as the expected subject. ### Claim 18 (depends on 1) The method of claim 1, wherein the expected subject is a subject associated with a particular device that captures the data segments at a location on the expected subject. ### Claim 19 (depends on 1) The method of claim 1, wherein the determining comprises taking account of one or both of a changing level of activity and a changing heart rate of the subject. ### Claim 20 (depends on 1) The method of claim 1, further comprising sending information to a device upon determining that the subject is the expected subject. ### Claim 21 (depends on 20) The method of claim 20, wherein the device is a payment gateway, and the information includes a payment authorization. ### Claim 22 (depends on 20) The method of claim 20, wherein the device is a lock, and the information causes the lock to unlock. ### Claim 23 (depends on 22) The method of claim 22, wherein causing the lock to unlock is further based on a location of the subject. ### Claim 24 (depends on 1) The method of claim 1, further comprising sending information to a device upon determining that the subject is under distress. ### Claim 25 (depends on 24) The method of claim 24, wherein the subject is determined to be under distress if one or more of a heart rate, a blood pressure, and a respiratory rate of the subject surpasses a threshold. ### Claim 26 (depends on 24) The method of claim 24, wherein the device is a payment gateway, and the information includes instructions for the payment gateway to prevent the subject from accessing the payment gateway. ### Claim 27 (depends on 24) The method of claim 24, wherein the device is a lock, and the information includes instructions for the lock to remain locked. ### Claim 28 (depends on 1) The method of claim 1, further comprising determining a pulse transit time (PTT) based on the datasets, the PTT representing a transit time of a pulse pressure wave within the subject. ### Claim 29 (depends on 1) The method of claim 1, further comprising determining a blood pressure of the subject based on the datasets. ### Claim 30 (depends on 1) The method of claim 1, wherein the determining comprises analyzing other biometric data including one or more of electrical skin impedance, respiratory rate, heart rate, heart rate variability, stroke volume, cardiac output, MoCG morphology, PPG morphology, and vocal sound frequency of the subject. ### Claim 31 (depends on 30) The method of claim 30, wherein analyzing the other biometric data comprises determining whether the subject is under distress. ### Claim 32 (depends on 1) The method of claim 1, wherein the morphological features comprise differences in blood pressure at specific times during each of the data segments. ### Claim 33 (depends on 32) The method of claim 32, wherein the specific times comprise times of peaks or valleys in blood pressure during the data segments. ### Claim 34 (depends on 32) The method of claim 32, wherein the morphological features comprise differences in blood pressure at successive peaks of blood pressure, successive valleys of blood pressure, or successive peaks and valleys of blood pressure. ### Claim 35 A method comprising: processing data in a first dataset that represents time-varying information about at least one pulse pressure wave propagating through blood in a subject acquired at a location of the subject; processing data in a second dataset that represents time-varying information about motion of the subject acquired at the location of the subject; based on the first and second datasets, determining at least two parameters of the subject, the parameters selected from the group consisting of blood pressure, respiratory rate, blood oxygen levels, heart rate, heart rate variability, stroke volume, cardiac output, MoCG morphology, and PPG morphology; determining a biometric signature of the subject, the biometric signature represented by a multi-dimensional space that is defined by at least two axes, each axis corresponding to at least one of the determined parameters; and determining whether the biometric signature was captured from a subject who is an expected subject by analyzing features of the biometric signature. ### Claim 36 A biofeedback device configured to be worn by a subject, the device comprising: a light source configured to emit light toward the skin of the subject; an optical sensor configured to: receive the emitted light after the emitted light reflects off of the skin of the subject; and provide data that corresponds to a characteristic of the received light, the data representing time-varying information about at least one pulse pressure wave propagating through blood in the subject acquired by the optical sensor at a location of the subject; a motion sensor configured to provide data that represents time-varying information about motion of the subject acquired by the motion sensor at the location of the subject; and a processor configured to: receive data from one or both of the light-emitting element and the optical sensor; receive data from the motion sensor; and determine whether one or more segments of the data were captured from a subject other than an expected subject by analyzing morphological features of the segments. ### Claim 37 One or more non-transitory machine-readable storage devices storing instructions that are executable by one or more processing devices to perform operations comprising: processing data in a first dataset that represents time-varying information about at least one pulse pressure wave propagating through blood in a subject acquired at a location of the subject; processing data in a second dataset that represents time-varying information about motion of the subject acquired at the location of the subject; and determining whether one or more segments of the datasets were captured from a subject other than an expected subject by analyzing morphological features of the segments, wherein the information about at least one pulse pressure wave propagating through blood in the subject comprises photoplethysmographic (PPG) data and the information about motion of the subject comprises one or both of motioncardiogram (MoCG) data and gross motion data. ### Claim 38 One or more non-transitory machine-readable storage devices storing instructions that are executable by one or more processing devices to perform operations comprising: processing data in a first dataset that represents time-varying information about at least one pulse pressure wave propagating through blood in a subject acquired at a location of the subject; processing data in a second dataset that represents time-varying information about motion of the subject acquired at the location of the subject; based on the first and second datasets, determining at least two parameters of the subject, the parameters selected from the group consisting of blood pressure, respiratory rate, blood oxygen levels, heart rate, heart rate variability, stroke volume, cardiac output, MoCG morphology, and PPG morphology; determining a biometric signature of the subject, the biometric signature represented by a multi-dimensional space that is defined by at least two axes, each axis corresponding to at least one of the determined parameters; and determining whether the biometric signature was captured from a subject who is an expected subject by analyzing features of the biometric signature.