DETECTING MEDICAL STATUS AND COGNITIVE IMPAIRMENT UTILIZING AMBIENT DATA

• US 20150257681A1
• Filed: 03/13/2015
• Published: 09/17/2015
• Est. Priority Date: 03/13/2014
• Status: Active Grant

• Current Assignee: IdeaFlood, Inc.
• Original Assignee: Brian Mark Shuster, Gary S. Shuster, Jr., Charles Marion Curry
• Inventors: SHUSTER, Gary Stephen, SHUSTER, Brian Mark, CURRY, Charles Marion Jr.
• Granted Patent: US 10,004,431 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• 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 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/082 Evaluation by breath analysis, e.g. determination of the chemical composition of exhaled breath A61B5/083, A61B5/091 take precedence
• 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 5/163 by tracking eye movement, gaze, or pupil change
• CPC Class Codes: A61B 5/18 for vehicle drivers or machine operators
• CPC Class Codes: A61B 5/4803 Speech analysis specially adapted for diagnostic purposes
• CPC Class Codes: A61B 5/4845 Toxicology, e.g. by detection of alcohol, drug or toxic products A61B5/4848 takes precedence
• CPC Class Codes: A61B 5/4863 Measuring or inducing nystagmus
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06T 19/006 Mixed reality object pose determination, tracking or camera calibration for mixed reality G06T7/00
• CPC Class Codes: G10L 15/01 Assessment or evaluation of speech recognition systems
• CPC Class Codes: G10L 25/66 for extracting parameters related to health condition detecting or measuring for diagnostic purposes A61B5/00
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Methods and systems for determining the likelihood that a person is impaired, generally comprising gathering ambient data from sensors operably coupled to a primary measuring device (PMD), analyzing and/or identifying the ambient data, querying at least one database to identify baseline data regarding the person, and comparing the ambient data to the baseline data to determine the likelihood that the person is impaired. The methods and systems advantageously detect medical and/or cognitive impairment without the need for specialized devices, examination by specialists, consent of the person being evaluated, and compliance with recommendations to be examined, and may be applied to estimating the level of impairment of a person due to alcohol, drugs, exposure to toxic chemicals, changes to mental status, insufficient sleep, medical problems, changes to blood sugar levels, divided attention, vision changes, hearing changes, use of a mobile device while walking or operating a vehicle, and other factors.

Claims

1. A method of determining the likelihood that a person is impaired, the method comprising:

gathering ambient data from one or more sensors operably coupled to a primary measuring device (PMD);

analyzing and/or identifying the ambient data;

querying at least one data base to identify baseline data regarding the person;

comparing the ambient data to the baseline data to determine a likelihood that the person is impaired.

2. The method of claim 1, wherein the method further comprises, gathering external device data from one or more external devices operably coupled to the PMD, and comparing the external device data to the baseline data to further determine the likelihood that the person is impaired.

3. The method of claim 2, wherein the one or more external devices comprise a motion-sensing watch, a head-mounted camera and/or a medical measurement device.

4. The method of claim 1, wherein the likelihood that the person is impaired is health related.

5. The method of claim 1, wherein the one or more sensors comprise a fingerprint sensor, an accelerometer, a three-axis gyroscope, an orientation sensor, an assisted GPS location sensor and/or an analog to digital sensor.

6. The method of claim 1, wherein the primary measuring device is a smartphone.

7. The method of claim 1, wherein the method also comprises estimating the level of impairment of the person.

8. The method of claim 1, further comprising converting the person'"'"'s voice to text and analyzing the text to further determine the likelihood that the person is impaired.

9. The method of claim 1, further comprising identifying predictable causes of changes to ambient data and using the predictable causes to confirm and/or modify the likelihood that the person is impaired.

10. A method of calibrating a PMD to determine impairment, the method comprising:

gathering calibration data about a person during one or more calibration periods; and

analyzing the calibration data to determine baseline data for the person, wherein the baseline data is configured to be compared against ambient data to determine a level of impairment of the person.

11. The method of claim 10, further comprising, operably coupling a breath alcohol measuring device to the PMD, and calibrating the PMD using the alcohol level measured by the breath alcohol measuring device.

12. The method of claim 10, further comprising, generating one or more profiles, and comparing ambient data to the profile(s) to further determine the level of impairment of the person, wherein the profiles are generated by analyzing data associated with one or more other persons with known or highly likely states of impairment.

13. The method of claim 10, further comprising, using input data to calibrate the PMD, wherein the input data is entered by the person and/or is retrieved from one or more storage devices containing characteristics of the person.

14. The method of claim 10, wherein the calibration data also comprises one or more sets of regularly observed patterns of the person.

15. The method of claim 14, further comprising, segregating the regularly observed patterns by time of day, location and/or task, and generating a set of exceptions to the baseline data from the segregated regularly observed patterns.

16. A device for determining the likelihood that a person is impaired, the device comprising:

one or more sensors operably coupled to a first PMD, the sensors configured to capture ambient data about a person;

wherein the first PMD is configured to query at least one data base to identify baseline data and compare the ambient data to the baseline data to determine the likelihood that the person is impaired.

17. The device of claim 16, further comprising a second PMD, and wherein the first PMD is configured to share ambient data with the second PMD, and the second PMD is configured to process the ambient data shared by the first PMD.

18. The device of claim 16, further comprising a plurality of PMD'"'"'s, wherein the plurality of PMD'"'"'s share data with each other.

19. The device of claim 16, wherein the first PMD is further configured to transmit notifications to one or more predetermined contacts.

20. The device of claim 16, further comprising at least one facial recognition device.