Biometric authentication

• US 9,396,643 B2
• Filed: 10/23/2014
• Issued: 07/19/2016
• Est. Priority Date: 10/23/2013
• Status: Expired due to Fees

• Current Assignee: Robert F. Dudley
• Original Assignee: Quanttus Inc
• Inventors: He, David Da, Bijjani, Richard Robehr, Azim, Shahid, Vargas, Bogart
• Primary Examiner(s): HUNNINGS, TRAVIS R
• Application Number:
  US14/522,230
• Publication Number:
  US 20150109124A1
• Time in Patent Office: 635 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• 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 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/02108 from analysis of pulse wave characteristics
• CPC Class Codes: A61B 5/02125 of pulse wave propagation time
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02427 Details of sensor
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0285 Measuring or recording phase velocity of blood waves
• CPC Class Codes: A61B 5/029 Measuring or recording blood output from the heart, e.g. minute volume A61B8/065 takes precedence
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• 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 5/1102 Ballistocardiography
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/113 occurring during breathing
• 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 5/14542 : for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/165 : Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/363 : Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/4809 : Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4815 : Sleep quality
• CPC Class Codes: A61B 5/4818 : Sleep apnoea
• CPC Class Codes: A61B 5/4833 : Assessment of subject's compliance to treatment
• CPC Class Codes: A61B 5/486 : Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/6801 : specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/681 : Wristwatch-type devices
• CPC Class Codes: A61B 5/6824 : Arm or wrist
• CPC Class Codes: A61B 5/6898 : Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: 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
• 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 5/742 : using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/746 : Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: G06F 21/32 : using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: 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
• CPC Class Codes: 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
• CPC Class Codes: G08B 21/06 : indicating a condition of sleep, e.g. anti-dozing alarms
• CPC Class Codes: 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

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

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.

2. The method of claim 1, wherein the data is acquired continuously.

3. The method of claim 1, wherein the data is acquired at a frequency of at least 16 Hz.

4. The method of claim 1, wherein the data is acquired at a frequency of between 75 Hz and 85 Hz.

5. The method of claim 1, wherein the data is acquired at a single location of the subject.

6. The method of claim 5, wherein the data is acquired by a device worn by the subject.

7. The method of claim 6, wherein the device is mobile and does not reduce a mobility of the subject.

8. The method of claim 6, wherein the device processes the data.

9. The method of claim 5, wherein the single location is an arm of the subject.

10. The method of claim 9, wherein the single location is a wrist of the subject.

11. 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.

12. The method of claim 11, wherein analyzing the other biometric data comprises determining whether the subject is under distress.

13. The method of claim 1, wherein the determining comprises analyzing confidential information provided by the subject.

14. The method of claim 13, wherein the confidential information includes one or more of a password, a personal identification number, and a predefined gesture.

15. The method of claim 1, wherein the analyzing comprises comparing morphological features of different segments of biometric data.

16. 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.

17. 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.

18. 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.

19. 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.

20. The method of claim 1, further comprising sending information to a device upon determining that the subject is the expected subject.

21. The method of claim 20, wherein the device is a payment gateway, and the information includes a payment authorization.

22. The method of claim 20, wherein the device is a lock, and the information causes the lock to unlock.

23. The method of claim 22, wherein causing the lock to unlock is further based on a location of the subject.

24. The method of claim 1, further comprising sending information to a device upon determining that the subject is under distress.

25. 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.

26. 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.

27. The method of claim 24, wherein the device is a lock, and the information includes instructions for the lock to remain locked.

28. 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.

29. The method of claim 1, further comprising determining a blood pressure of the subject based on the datasets.

30. 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.

31. The method of claim 30, wherein analyzing the other biometric data comprises determining whether the subject is under distress.

32. The method of claim 1, wherein the morphological features comprise differences in blood pressure at specific times during each of the data segments.

33. The method of claim 32, wherein the specific times comprise times of peaks or valleys in blood pressure during the data segments.

34. 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.

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.

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.

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.

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.