Method and system for behavioral monitoring

• US 10,321,870 B2
• Filed: 04/30/2015
• Issued: 06/18/2019
• Est. Priority Date: 05/01/2014
• Status: Active Grant

• Current Assignee: Ramot at Tel Aviv University Limited (Tel Aviv University)
• Original Assignee: Ramot at Tel Aviv University Limited (Tel Aviv University)
• Inventors: Nevo, Uri, Zeevy Sela, Keren, Liberman, Asaf, Erlich, Dana, Harel, Adam, Marton, Jonathan
• Primary Examiner(s): Henson, Devin B
• Application Number:
  US14/700,277
• Publication Number:
  US 20150313529A1
• Time in Patent Office: 1,510 Days
• 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 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/22 Ergometry; Measuring muscular strength or the force of a muscular blow exercising apparatus A63B21/00 ; measuring of work or force in general G01L
• CPC Class Codes: A61B 5/225 of the fingers, e.g. by monitoring hand-grip force
• CPC Class Codes: A61B 5/4803 Speech analysis specially adapted for diagnostic purposes
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• 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 5/7275 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: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A method of analysis is disclosed. The method comprises: receiving from a mobile device of a subject sensor data and/or device state data, analyzing the data to provide at least one behavioral pattern associated with the subject, comparing the behavioral pattern with a reference behavioral pattern, and estimating the likelihood that the subject is experiencing or is expected to experience an abnormal condition based on the comparison.

Claims

1. A method of estimating likelihood that a subject is experiencing or is expected to experience an abnormal condition, the method comprising:

automatically receiving from a mobile device of the subject, over a communication network, sensor data and device state data selected from the group consisting of electronic communication log data, screen state data, power on/off data, and battery state data, and storing said sensor data and device state data in a computer readable memory;

analyzing said sensor data and device state data by a remote computer to provide at least one behavioral pattern associated with said subject, and storing said at least one behavioral pattern in said computer readable memory;

comparing said behavioral pattern with a reference behavioral pattern; and

estimating the likelihood that said subject is experiencing or is expected to experience an abnormal condition based on said comparison, and transmitting over said communication network an alert pertaining to said estimated likelihood to said mobile device of the subject;

wherein said analyzing comprises (i) estimating sleep offset from motion sensors of said mobile device and/or from device state data of said mobile device, said sleep offset being defined as a time difference between two sleep onset times according to a local clock, and (ii) including said estimated sleep offset as a parameter for said behavioral pattern, and wherein said estimating said likelihood is based in part on said parameter; and

wherein said estimating the likelihood that said subject is experiencing or is expected to experience an abnormal condition, comprises estimating the likelihood that said subject is experiencing or is expected to experience a manic episode.

2. The method according to claim 1, wherein said analyzing comprises determining level of physical activeness of said subject.

3. The method of claim 2, wherein said determining said level of activeness comprises measuring speed of movement of said subject.

4. The method of claim 2, wherein said sensor data comprise location data and/or acceleration data, and wherein said determining said level of activeness comprises identifying periodic movements of said subject, based on said location data and/or said acceleration data.

5. The method according to claim 1, wherein said sensor data comprises audio data, and wherein said analysis comprises identifying speech compression in said audio data.

6. The method according to claim 1, wherein said analyzing comprises estimating sleep duration.

7. The method according to claim 1, wherein said analyzing comprises identifying temporary awakening periods.

8. The method according to claim 1, wherein said analyzing comprises calculating a score pertaining to activity level of said subject over at least one communication network.

9. The method according to claim 1, wherein said analyzing comprises executing a voice analysis procedure.

10. The method according to claim 9, wherein said voice analysis procedure is executed to identify speech compression event.

11. The method according to claim 1, wherein said sensor data comprise at least two different types of data.

12. The method according to claim 1, wherein said sensor data comprise at least three different types of data.

13. The method according to claim 1, wherein said sensor data comprise at least four different types of data.

14. The method according to claim 1, wherein said sensor data comprise data selected from the group consisting of location data, acceleration data, orientation data, audio data, ambient illumination data.

15. The method according to claim 1, wherein said device state data comprise data selected from the group consisting of at least one of electronic communication log data and screen state data.

16. The method according to claim 1, wherein said analyzing comprises executing a machine learning procedure.

17. The method of claim 16, wherein said machine learning procedure comprises a supervised learning procedure.

18. The method according to claim 16, wherein said machine learning procedure comprises at least one procedure selected from the group consisting of clustering, support vector machine, linear modeling, k-nearest neighbors analysis, decision tree learning, ensemble learning procedure, neural networks, probabilistic model, graphical model, Bayesian network, and association rule learning.

19. The method of claim 1, wherein said mobile device comprises a touch screen, wherein said analysis comprises:

(i) determining pressure applied by said subject to said touch screen, said determining said pressure comprising measuring an area of said touch screen touched by said subject, and (ii) including said determined pressure as a pressure parameter for said behavioral pattern, and wherein said estimating said likelihood is based in part on said pressure parameter.

20. The method according to claim 1, wherein said receiving said sensor data comprises remotely controlling said mobile device so as to vary a sampling rate of sensors of said mobile device.

21. A method of determining a mental condition of a subject, the method comprising:

automatically receiving from a mobile device of the subject, over a communication network, sensor data and device state data selected from the group consisting of electronic communication log data, screen state data, power on/off data, and battery state data, and storing said sensor data and device state data in a computer readable memory;

analyzing said sensor data and device state data by a remote computer to provide at least one behavioral pattern associated with said subject, and storing said at least one behavioral pattern in said computer readable memory;

comparing said behavioral pattern with a reference behavioral pattern;

determining a mental condition of the subject based on said comparison; and

over said communication network, transmitting to the mobile device of the subject an output indicative of said mental condition to be displayed by said mobile device;

wherein said analyzing comprises (i) estimating a sleep offset from motion sensors of said mobile device and/or from device state data of said mobile device, said sleep offset being defined as a time difference between two sleep onset times, and (ii) including said estimated sleep offset as a parameter for said behavioral pattern, and wherein said estimating said mental condition is based in part on said parameter; and

wherein said mental condition is a mood disorder or a mood change.

22. The method of claim 21, wherein said providing said output comprises displaying said mental condition on a display of said mobile device.

23. The method of claim 21, wherein said mood disorder is selected from the group consisting of Substance-Induced Mood Disorder, Alcohol-Induced Mood Disorder, Amphetamine-Induced Mood Disorder, Cocaine-Induced Mood Disorder, Hallucinogen-Induced Mood Disorder, Inhalant-Induced Mood Disorder, Opioid-Induced Mood Disorder, Phencyclidine-Induced Mood Disorder, and Sedative-, Hypnotic- or Anxiolytic-Induced Mood Disorder.