Medical evaluation system and method using sensors in mobile devices

• US 9,872,637 B2
• Filed: 04/21/2011
• Issued: 01/23/2018
• Est. Priority Date: 04/21/2010
• Status: Active Grant

• Current Assignee: Northwestern University, Rehabilitation Institute of Chicago (Tenet Healthcare Corporation)
• Original Assignee: Northwestern University, Rehabilitation Institute of Chicago (Tenet Healthcare Corporation)
• Inventors: Kording, Konrad, Albert, Mark, Levien, Andrew
• Primary Examiner(s): HINDENBURG, MAX F
• Application Number:
  US13/642,219
• Publication Number:
  US 20130041290A1
• Time in Patent Office: 2,469 Days
• Field of Search: 600595
• CPC Class Codes: A61B 5/1101 Detecting tremor
• CPC Class Codes: A61B 5/1126 using a particular sensing technique
• 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 5/4082 Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A medical evaluation system includes an I/O module, a processing module, and an analysis module. The I/O module receives sensory data obtained by a motion sensor disposed in a mobile device carried by a patient at least when the patient is in a non-clinical environment. The processing module extracts medically relevant data from the sensory data received from the sensor in the mobile device. The relevant data includes one or more features of interest in the sensory data. The analysis module derives one or more surrogate biomarkers from the relevant data. The surrogate biomarkers represent at least one of a state or a progression of a medical condition of the patient. The mobile device may be a mobile phone carried by the patient and the sensor may include at least one of an accelerometer or a gyroscope that generates the sensory data to represent movements of the patient.

Claims

1. A mobile computing device comprising:

an alarm indicator,

a motion sensor that detects motion of the patient and generates sensory data representing the patient motion,

a computer processor configured to;

extract medically relevant data from the sensory data, the relevant data including one or more features of interest in the sensory data,

derive one or more surrogate biomarkers from the relevant data, the surrogate biomarkers representative of at least one of a state of a medical condition or a progression of a medical condition of the patient,

identify the patient as a specific patient; and

provide a real-time intervention alarm through the alarm indicator when the surrogate biomarkers approach a danger level or threshold that indicates a likelihood of an adverse event occurring to the patient, wherein the real-time intervention alarm is provided prior to the occurrence of the adverse event,

wherein the real-time intervention alarm is based on one or more changes in the relevant data and includes a notification directing the patient to change a current activity of the patient.

2. The system of claim 1, wherein the mobile computing device is a mobile phone configured to be carried by the patient, and wherein the motion sensor includes at least one of an accelerometer or a gyroscope that generates the sensory data to represent movements of the patient.

3. The system of claim 1, wherein the computer processor is configured to employ one or more machine learning algorithms that identify one or more patterns or trends in the relevant data to derive the surrogate biomarkers.

4. The system of claim 1, wherein the surrogate biomarkers include an estimate of a quantitative clinical score in a medical ranking system.

5. The system of claim 4, wherein the one or more estimates of quantitative clinical scores include at least one of an indication of early onset or a probability of early onset of a disease of the patient.

6. The system of claim 1, wherein the computer processor is configured to determine one or more of the features of interest in the sensory data as the relevant data and use the relevant data to identify the patient carrying the mobile computing device.

7. The system of claim 1, wherein the medical condition includes at least one of Parkinson'"'"'s disease, spinal cord injury, multiple sclerosis, cerebral palsy, or cerebellar ataxia.

8. The system of claim 1, wherein the surrogate biomarker is representative of at least one of a degree of activity of the patient, gait of the patient, sway of the patient, tremor of the patient, deviation of the patient from steady behavior, recovery from perturbation, exercise, wheelchair management, and motorized transport use.

9. The system of claim 1, wherein the computer processor is configured to determine a posture of the patient based on the sensory data.

10. The system of claim 1, wherein the computer processor is configured to transmit a report including the surrogate biomarker to one or more of an insurance company, a family member of the patient, or a friend of the patient.

11. A method comprising:

a mobile computing device measuring sensory data representative of movement of a patient using a motion sensor of the mobile computing device when carried by the patient in a non-clinical environment;

with the mobile computing device,

extracting the medically relevant data from the sensory data by identifying one or more features of interest in the sensory data;

deriving a surrogate biomarker from the relevant data, wherein the surrogate biomarker represents at least one of a state or a progression of the medical condition of the patient;

identifying the patient as a specific patient; and

providing a real-time intervention alarm through an alarm indicator coupled to the computing device when the surrogate biomarkers approach a danger level or threshold that indicates a likelihood of an adverse event occurring to the patient, wherein the real-time intervention alarm is provided prior to the occurrence of the adverse event,

wherein the real-time intervention alarm is based on one or more changes in the relevant data and includes a notification directing the patient to change a current activity of the patient.

12. The method of claim 11, wherein the mobile computing device is a mobile phone carried by the patient and the sensor includes at least one of an accelerometer or a gyroscope that generates the sensory data to represent movements of the patient.

13. The method of claim 11, wherein deriving the surrogate biomarker includes employing one or more machine learning algorithms that identify at least one pattern or trend in the relevant data to derive the surrogate biomarker.

14. The method of claim 11, wherein the surrogate biomarker includes at least one of an indication of early onset or a probability of early onset of a disease of the patient.

15. The method of claim 11, further comprising determining one or more of the features of interest in the sensory data as the relevant data and identifying the patient carrying the mobile computing device using the relevant data.

16. The method of claim 11, further comprising determining a location where the mobile computing device is carried by the patient by comparing the relevant data with one or more location signatures associated with different locations wherein the mobile device is carried on the patient, the location signatures including one or more of the features of interest of previously acquired sensory data from the sensor when the device is carried in the different locations on the patient.

17. The method of claim 11, wherein the medical condition includes at least one of Parkinson'"'"'s disease, spinal cord injury, multiple sclerosis, cerebral palsy, or cerebellar ataxia.

18. The method of claim 11, wherein the surrogate biomarker is representative of at least one of a degree of activity of the patient, gait of the patient, sway of the patient, tremor of the patient, deviation of the patient from steady behavior, recovery from perturbation, exercise, wheelchair management, and motorized transport use.

19. The method of claim 11, wherein the surrogate biomarker represents a posture of the patient.

20. A non-transitory computer readable storage medium having stored thereon one or more sets of instructions that direct a mobile computing device to:

receive sensory data obtained by a motion sensor in the mobile computing device while the mobile computing device is carried by a patient in a non-clinical environment;

extract medically relevant data from the sensory data by identifying one or more features of interest in the sensory data;

derive a surrogate biomarker from the relevant data, wherein the surrogate biomarker represents of at least one of a state of a medical condition or a progression of a medical condition of the patient;

identify the patient as a specific patient; and

provide a real-time intervention alarm through an alarm indicator coupled to the mobile computing device when the surrogate biomarkers approach a danger level or threshold that indicates a likelihood of an adverse event occurring to the patient, wherein the real-time intervention alarm is provided prior to the occurrence of the adverse event,

wherein the real-time intervention alarm is based on one or more changes in the relevant data and includes a notification directing the patient to change a current activity of the patient.

21. The non-transitory computer readable storage medium of claim 20, wherein the mobile computing device is a mobile phone carried by the patient and the motion sensor includes at least one of an accelerometer or a gyroscope that generates the sensory data to represent movements of the patient.

22. The non-transitory computer readable storage medium of claim 20, wherein the sets of instructions direct the mobile computing device to:

employ one or more machine learning algorithms that identify at least one of a pattern or a trend in the relevant data, and the surrogate biomarker is based on the at least one of the pattern or the trend.

23. The non-transitory computer readable storage medium of claim 20, wherein the surrogate biomarker includes at least one of an indication of early onset or a probability of early onset of a disease of the patient.

24. The non-transitory computer readable storage medium of claim 20, wherein the one or more sets of instructions direct the mobile computing device to determine one or more of the features of interest in the sensory data as the relevant data and to identify the patient carrying the mobile computing device based on the features of interest.

25. The non-transitory computer readable storage medium of claim 20, wherein the one or more sets of instructions direct the mobile computing device to determine a location that the mobile computing device is carried by the patient by comparing the relevant data with one or more location signatures associated with different locations on the patient, the location signatures including one or more of the features of interest of previously acquired sensory data from the sensor when the mobile computing device is carried in the different locations on the patient.

26. The non-transitory computer readable storage medium of claim 20, wherein the real-time intervention alarm comprises a fall prevention alarm.

27. The non-transitory computer storage medium of claim 26, further including one or more sets of instructions that direct the mobile computing device to provide the fall prevention alarm to a person other than the patient.

28. The non-transitory computer storage medium of claim 27, wherein the person other than the patient is a pre-identified family member or friend.

29. The non-transitory computer storage medium of claim 27, wherein the person other than the patient is a clinician.

30. The non-transitory computer storage medium of claim 27, wherein the person other than the patient is an emergency personnel.

31. The non-transitory computer storage medium of claim 26, wherein the fall prevention alarm is a tactile alarm.

32. The non-transitory computer storage medium of claim 26, wherein the mobile computing device is fixed to an appendage of the patient.

33. The non-transitory computer storage medium of claim 26, wherein the medical condition is a fall risk condition and the medically relevant data comprises acceleration data.