SYSTEM COMPRISED OF SENSORS, COMMUNICATIONS, PROCESSING AND INFERENCE ON SERVERS AND OTHER DEVICES

• US 20130218053A1
• Filed: 07/08/2011
• Published: 08/22/2013
• Est. Priority Date: 07/09/2010
• Status: Abandoned Application

• Current Assignee: Regents of the University of California (University of California)
• Original Assignee: Regents of the University of California (University of California)
• Inventors: Kaiser, William J., Sarrafzadeh, Majid, Dobkin, Bruce H., Pottie, Greg, Batalin, Maxim A., Xu, Xiaoyu
• Application Number:
  US13/809,399
• Publication Number:
  US 20130218053A1
• Time in Patent Office: Days
• US Class Current: 600/595
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• 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 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A system for monitoring patient activity comprising: at least one measurement device configured to provide data related to a patient'"'"'s physical activity; and a server configured to make an inference regarding the patient'"'"'s physical activity based on data provided by the at least one measurement device. In some embodiments, the inference is a determination of a type of physical activity. In some embodiments, the measurement device is configured to be worn by the patient or carried in the patient'"'"'s pocket. In some embodiments, two or more measurement devices are used. In some embodiments, the server is remotely located from the measurement device. In some embodiments, the server is configured to archive and retrieve the data provided by the measurement device and the inferences.

Claims

1. A system for monitoring patient activity comprising:

at least one measurement device configured to provide data related to a patient'"'"'s physical activity; and

a server configured to make an inference regarding the patient'"'"'s physical activity based on data provided by the at least one measurement device.

2. The system of claim 1, wherein the inference is a determination of a type of physical activity.

3. The system of claim 1, wherein the at least one measurement device is configured to provide the data related to the patient'"'"'s physical activity from a location remote from the server.

4. The system of claim 1, wherein the at least one measurement device is configured to be worn by the patient or carried in the patient'"'"'s pocket.

5. The system of claim 1, wherein the at least one measurement device is configured to transmit the data related to the patient'"'"'s physical activity via wireless communication.

6. The system of claim 1, wherein the at least one measurement device comprises two or more measurement devices each configured to provide data related to the patient'"'"'s physical activity.

7. The system of claim 1, wherein the at least one measurement device comprises a triaxial accelerometer, a microgyroscope, or a pressure sensor.

8. The system of claim 1, wherein the at least one measurement device is configured to automatically take repeated data samples.

9. The system of claim 1, wherein the server is configured to infer the probability of a patient being in an activity state based on the data provided by the at least one measurement device.

10. The system of claim 1, wherein the server is configured to make the inference based on a combination of data obtained from different measurement devices corresponding to different parts of the patient'"'"'s body.

11. The system of claim 10, wherein the data in the combination of data is based on samples being taken simultaneously by the different measurement devices.

12. The system of claim 1, wherein the server is configured to make the inference by applying Bayesian Sensor Fusion analysis in making the inference.

13. The system of claim 12, wherein the server is configured to apply a naive Bayer classifier model to infer the probability of a patient state vector given a feature vector.

14. The system of claim 1, wherein the server is configured to use a Fourier transform in processing data provided by the at least one measurement device in a time domain to extract frequency spectral components.

15. The system of claim 14, wherein the server is configured to use a Fast Fourier transform.

16. The system of claim 1, wherein the server is configured to make the inference by using a fundamental frequency component and spectrum energy.

17. The system of claim 1, wherein the server is configured to make the inference by applying one or more motion recognition algorithms

18. The system of claim 1, wherein the server is configured to make the inference by applying one or more state classification algorithms to make the inference.

19. The system of claim 1, wherein the server is configured to archive and retrieve the data provided by the at least one measurement device and the inferences.

20. A method of monitoring patient activity, the method comprising:

a server receiving data related to a patient'"'"'s physical activity, wherein the data is based on one more samples from at least one measurement device; and

the server making an inference regarding the physical activity based on the received data

21. The method of claim 20, wherein the inference is a determination of a type of physical activity.

22. The method of claim 20, wherein the server is located remotely from the at least one measurement device.

23. The method of claim 20, wherein the step of the server receiving the data is preceded by a step of the at least one measurement device taking one or more samples of the patient'"'"'s physical activity.

24. The method of claim 23, wherein the at least one measurement device is worn by the patient or carried in the patient'"'"'s pocket when the one or more samples are taken.

25. The method of claim 23, wherein the at least one measurement device transmits the data via a wireless communication.

26. The method of claim 23, wherein the at least one measurement device comprises a triaxial accelerometer, a microgyroscope, or a pressure sensor.

27. The method of claim 23, wherein the at least one measurement device automatically takes repeated data samples.

28. The method of claim 20, wherein the server infers the probability of a patient being in an activity state based on the data provided by the at least one measurement device.

29. The method of claim 20, wherein the server makes the inference based on a combination of data obtained from different measurement devices corresponding to different parts of the patient'"'"'s body.

30. The method of claim 29, wherein the data in the combination of data is based on samples being taken simultaneously by the different measurement devices.

31. The method of claim 20, wherein the server applies Bayesian Sensor Fusion analysis in making the inference.

32. The method of claim 31, wherein the server applies a naive Bayer classifier model to infer the probability of a patient state vector given a feature vector.

33. The method of claim 20, wherein the server uses a Fourier transform in processing data provided by the at least one measurement device in a time domain to extract frequency spectral components.

34. The method of claim 33, wherein the server uses a Fast Fourier transform.

35. The method of claim 20, wherein the server makes the inference by using a fundamental frequency component and spectrum energy.

36. The method of claim 20, wherein the server makes the inference by applying one or more motion recognition algorithms

37. The method of claim 20, wherein the server makes the inference by applying one or more state classification algorithms

38. The method of claim 20, further comprising the server archiving the received data and the inferences for subsequent retrieval.

39. A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform a method of monitoring patient activity, the method comprising:

making an inference regarding a patient'"'"'s physical activity based on data related to the patient'"'"'s physical activity, wherein the data is based on one more samples from at least one measurement device.

40. The device of claim 39, wherein the inference is a determination of a type of physical activity.

41. The device of claim 39, wherein making the inference comprises inferring the probability of a patient being in an activity state based on the data provided by the at least one measurement device.

42. The device of claim 39, wherein the inference is based on a combination of data obtained from different measurement devices corresponding to different parts of the patient'"'"'s body.

43. The device of claim 42, wherein the data in the combination of data is based on samples that have been taken simultaneously by the different measurement devices.

44. The device of claim 39, wherein making the inference comprises applying Bayesian Sensor Fusion analysis.

45. The device of claim 44, wherein the method further comprises applying a naï

ve Bayer classifier model to infer the probability of a patient state vector given a feature vector.

46. The device of claim 39, wherein the method further comprises using a Fourier transform in processing data provided by the at least one measurement device in a time domain to extract frequency spectral components.

47. The device of claim 46, wherein the method further comprises using a Fast Fourier transform in processing data

48. The device of claim 39, wherein making the inference comprises using a fundamental frequency component and spectrum energy.

49. The device of claim 39, wherein making the inference comprises applying one or more motion recognition algorithms

50. The device of claim 39, wherein making the inference comprises applying one or more state classification algorithms

51. The device of claim 39, wherein the method further comprises archiving the received data and the inferences for subsequent retrieval.

52. A system for training a model for monitoring patient activity, the system comprising a server configured to:

extract features from training data;

cluster the extracted features into a discrete feature space; and

perform a maximum likelihood estimation for the discrete feature space to construct a maximum likelihood model.

53. The system of claim 52, wherein the server is configured to cluster the extracted features using Gaussian cluster discretization.

54. The system of claim 52, wherein the server is further configured to correlate features with different states of activity for a patient.

55. A method of training a model for monitoring patient activity, the method comprising:

extracting features from training data;

clustering the extracted features into a discrete feature space; and

performing a maximum likelihood estimation for the discrete feature space to construct a maximum likelihood model.

56. The method of claim 55, wherein clustering the extracted features comprises performing Gaussian cluster discretization.

57. The method of claim 55, further comprising the step of correlating features with different states of activity for a patient.

58. A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform a method of training a model for monitoring patient activity, the method comprising:

extracting features from training data;

clustering the extracted features into a discrete feature space; and

performing a maximum likelihood estimation for the discrete feature space to construct a maximum likelihood model.

59. The device of claim 58, wherein clustering the extracted features comprises performing Gaussian cluster discretization.

60. The device of claim 58, wherein the method further comprises the step of correlating features with different states of activity for a patient.