Systems, environment and methods for identification and analysis of recurring transitory physiological states and events using a portable data collection device

• US 9,936,916 B2
• Filed: 04/22/2015
• Issued: 04/10/2018
• Est. Priority Date: 10/09/2013
• Status: Active Grant

• Current Assignee: Nedim T. Sahin
• Original Assignee: Nedim T. Sahin
• Inventors: Sahin, Nedim T.
• Primary Examiner(s): Downey, John R
• Application Number:
  US14/693,641
• Publication Number:
  US 20150223731A1
• Time in Patent Office: 1,084 Days
• Field of Search: 600300, 600301, 600595
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• 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 5/16 Devices for psychotechnics using teaching or educational appliances G09B1/00 - G09B7/00; Testing reaction times ; Devices for evaluating the psychological state
• CPC Class Codes: A61B 5/163 by tracking eye movement, gaze, or pupil change
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/6803 Head-worn items, e.g. helmets, masks, headphones or goggles
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: G16H 40/67 for remote operation

Abstract

In one aspect, the systems, environment, and methods described herein support anticipation and identification of adverse health events and/or atypical behavioral episodes such as Autistic behaviors, epileptic seizures, heart attack, stroke, and/or narcoleptic “sleep attacks” using a portable data collection device. In another aspect, the systems, environment, and methods described herein support measurement of motions and vibrations associated with recurring transitory physiological states and events using a portable data collection device. For example, motion and vibration measurements may be analyzed to identify pronounced head motion patterns indicative of specific heart defects, neurodegenerative conditions, inner ear or other balance problems, or types of cardiac disease. In another example, motion and vibration measurements may be analyzed to identify slow-wave changes indicative of temporary conditions such as intoxication, fatigue, distress, aggression, attention deficit, anger, and/or narcotic ingestion as well as temporary or periodic normal events, such as ovulation, pregnancy, and sexual arousal.

Claims

1. A system comprising:

a portable data collection device designed to be held by a user or worn upon a body of the user, the data collection device comprising

processing circuitry, and

a non-transitory computer readable medium having instructions stored thereon; and

one or more input capture elements connected to and/or in communication with the portable data collection device, wherein the one or more input capture elements are positioned upon or proximate to a head of the user or directed toward the head of the user;

wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to;

collect, over a period of time via at least one of the one or more input capture elements, sensor data, wherein the sensor data includes at least one of image data, audio data, electromagnetic data, and motion data,

analyze the sensor data to identify a time progression of measurements including at least one of a) a plurality of involuntary small motion measurements, and b) a plurality of vibration measurements,

analyze the time progression of measurements to identify a physiological pattern, wherein

the physiological pattern comprises at least one of a pronounced head motion pattern and a slow-wave change pattern, and

the physiological pattern is based at least in part on at least one of a) the plurality of involuntary small motion measurements and b) the plurality of vibration measurements,

analyze the physiological pattern to identify a physiological state indicated by the physiological pattern, and

provide, to at least one of a user of the portable data collection device and a third party computing device, feedback corresponding to the physiological pattern, wherein

providing feedback to the user comprises providing feedback responsive to the physiological state indicated by the physiological pattern, wherein

the feedback comprises at least one of visual, audible, haptic, and neural stimulation feedback to the user, and

the feedback is provided via at least one output feature of one or more output features of the portable data collection device, and

providing feedback to the third party computing device comprises transmitting, via a wired or wireless transmission link, a data transmission to the third party computing device identifying at least one of the physiological pattern and an identification of a physiological state indicated by the physiological pattern.

2. The system of claim 1, wherein the physiological state comprises at least one of a chronic anomalous physiological state and a temporary anomalous event state.

3. The system of claim 2, wherein the chronic anomalous physiological state comprises one or more of Autistic behaviors, a heart defect, a neurodegenerative condition, an inner ear problem, a balance problem, and a type of cardiac disease.

4. The system of claim 1, wherein analyzing the time progression of measurements to identify the physiological pattern comprises analyzing the time progression of measurements in light of additional data captured during or corresponding to the period of time, wherein the additional data is at least one of a) external to the user and b) related to the user.

5. The system of claim 4, wherein additional data external to the user comprises one or more of circadian rhythm data, seasonal data, audio data capturing background noise of a surroundings of the user light intensity data capturing light patterns of a surroundings of the user, and location data capturing one or more positions of the user.

6. The system of claim 4, wherein additional data related to the user comprises one or more of an activity pattern of the user, audio data capturing vocalizations of the user, pharmaceutical intake of the user, stimulant intake of the user, a neural state of the user, and dietary intake of the user.

7. The system of claim 4, wherein the instructions, when executed by the processing circuitry, further cause the processing circuitry to collect over the period of time via at least one of one or more additional input capture elements, at least a portion of the additional data.

8. The system of claim 1, wherein identifying the physiological pattern comprises:

accessing a baseline physiological pattern associated with at least one of the user and a group of individuals sharing one or more features of the user; and

analyzing the time progression of measurements in view of the baseline physiological pattern to identify one or more divergences or anomalies.

9. The system of claim 8, wherein the one or more features of the user comprise at least one of a physiological disability, a sensory sensitivity, and a digestive disorder diagnosis.

25. The system of claim 1, wherein the plurality of vibration measurements comprises sound of breathing.

26. The system of claim 1, wherein the instructions, when executed by the processing circuitry, cause the processing circuitry to store, upon a non-transitory computer readable storage device, the physiological pattern.

27. The system of claim 1, wherein the data collection device is designed to be worn upon the head of the user.

10. A non-transitory computer readable medium having instructions stored thereon for monitoring physiological patterns of a user of a portable data collection device, wherein the instructions, when executed by processing circuitry, cause the processing circuitry to:

collect, over a period of time via at least one of one or more input capture elements, sensor data, wherein the sensor data includes at least one of image data, audio data, electromagnetic data, and motion data, wherein

the one or more input capture elements connected to and/or in communication with the portable data collection device, and

the one or more input capture elements are positioned upon or proximate to a head of the user or directed toward the head of the user;

analyze the sensor data to identify a time progression of measurements including at least one of a) a plurality of involuntary small motion measurements, and b) a plurality of vibration measurements;

analyze the time progression of measurements to identify a physiological pattern, wherein

the physiological pattern comprises at least one of a pronounced head motion pattern and a slow-wave change pattern, and

the physiological pattern is based at least in part on at least one of a) the plurality of involuntary small motion measurements and b) the plurality of vibration measurements;

analyze the physiological pattern to identify a physiological state indicated by the physiological pattern; and

provide, to at least one of a user of the portable data collection device and a third party computing device, feedback corresponding to the physiological pattern, wherein

providing feedback to the user comprises providing feedback responsive to the physiological state indicated by the physiological pattern, wherein

the feedback comprises at least one of visual, audible, haptic, and neural stimulation feedback to the user, and

the feedback is provided via at least one output feature of one or more output features of the portable data collection device, and

providing feedback to the third party computing device comprises transmitting, via a wired or wireless transmission link, a data transmission to the third party computing device identifying at least one of the physiological pattern and the identification of the physiological state.

11. The non-transitory computer readable medium of claim 10, wherein analyzing the time progression of measurements to identify the physiological pattern comprises analyzing the time progression of measurements in light of additional data captured during or corresponding to the period of time, wherein the additional data is at least one of a) external to the user and b) related to the user.

12. The non-transitory computer readable medium of claim 11, wherein additional data external to the user comprises one or more of circadian rhythm data, seasonal data, audio data capturing background noise of a surroundings of the user, light intensity data capturing light patterns of a surroundings of the user, and location data capturing one or more positions of the user.

13. The non-transitory computer readable medium of claim 11, wherein additional data related to the user comprises one or more of an activity pattern of the user, audio data capturing vocalizations of the user, pharmaceutical intake of the user, stimulant intake of the user, a neural state of the user, and dietary intake of the user.

14. The non-transitory computer readable medium of claim 11, wherein the instructions, when executed by the processing circuitry, further cause the processing circuitry to collect over the period of time via at least one of one or more additional input capture elements, at least a portion of the additional data.

15. The non-transitory computer readable medium of claim 10, wherein identifying the physiological pattern comprises:

accessing a baseline physiological pattern associated with at least one of the user and a group of individuals sharing one or more features of the user; and

analyzing the time progression of measurements in view of the baseline physiological pattern to identify one or more divergences or anomalies.

16. The non-transitory computer readable medium of claim 15, wherein the one or more features of the user comprise at least one of a physiological disability, a sensory sensitivity, and a digestive disorder diagnosis.

17. A method for monitoring physiological patterns of a user of a portable data collection device configured to be worn upon a body of the user or held by the user, the method comprising:

collecting, over a period of time via at least one of one or more input capture elements, sensor data, wherein the sensor data includes at least one of image data, audio data, electromagnetic data, and motion data, wherein

the one or more input capture elements are connected to and/or in communication with the portable data collection device, and

the one or more input capture elements are positioned upon or proximate to a head of the user or directed toward the head of the user;

analyzing, by processing circuitry, the sensor data to identify a time progression of measurements including a) a plurality of involuntary small motion measurements and b) a plurality of vibration measurements;

analyzing, by the processing circuitry, the time progression of measurements to identify a physiological pattern, wherein

the physiological pattern comprises at least one of a pronounced head motion pattern and a slow-wave change pattern, and

the physiological pattern is based at least in part on at least one of a) the plurality of involuntary small motion measurements and b) the plurality of vibration measurements;

analyzing, by the processing circuitry, the physiological pattern to identify a physiological state indicated by the physiological pattern; and

providing, by the processing circuitry to at least one of a user of the portable data collection device and a third party computing device, feedback corresponding to the physiological pattern, wherein

providing feedback to the user comprises providing feedback responsive to the physiological state indicated by the physiological pattern, wherein

the feedback comprises at least one of visual, audible, haptic, and neural stimulation feedback to the user, and

the feedback is provided via at least one output feature of one or more output features of the portable data collection device, and

providing feedback to the third party computing device comprises transmitting, via a wired or wireless transmission link, a data transmission to the third party computing device identifying at least one of the physiological pattern and the identification of the physiological state.

18. The method of claim 17, wherein the physiological state comprises at least one of a chronic anomalous physiological state and a temporary anomalous event state.

19. The method of claim 18, wherein the chronic anomalous physiological state comprises one or more of Autistic behaviors, a heart defect, a neurodegenerative condition, an inner ear problem, a balance problem, and a type of cardiac disease.

20. The method of claim 17, wherein analyzing the time progression of measurements to identify the physiological pattern comprises analyzing the time progression of measurements in light of additional data captured during or corresponding to the period of time, wherein the additional data is at least one of a) external to the user and b) related to the user.

21. The method of claim 20, wherein additional data external to the user comprises one or more of circadian rhythm data, seasonal data, audio data capturing background noise of a surroundings of the user, light intensity data capturing light patterns of a surroundings of the user, and location data capturing one or more positions of the user.

22. The method of claim 20, wherein additional data related to the user comprises one or more of an activity pattern of the user, audio data capturing vocalizations of the user, pharmaceutical intake of the user, stimulant intake of the wearer, a neural state of the user, and dietary intake of the user.

23. The method of claim 20, further comprising collecting, over the period of time via at least one of one or more additional input capture elements, at least a portion of the additional data.

24. The method of claim 17, wherein identifying the physiological pattern comprises:

accessing a baseline physiological pattern associated with at least one of the user and a group of individuals sharing one or more features of the user; and

analyzing the time progression of measurements in view of the baseline physiological pattern to identify one or more divergences or anomalies.