Method and apparatus for detecting disease regression through network-based gait analysis

• US 9,445,769 B2
• Filed: 12/06/2013
• Issued: 09/20/2016
• Est. Priority Date: 12/06/2013
• Status: Active Grant

• Current Assignee: AT&T Intellectual Property I LP (AT&T, Inc.), President and Fellows of Harvard College (Harvard Corporation)
• Original Assignee: AT&T Intellectual Property I LP (AT&T, Inc.), President and Fellows of Harvard College (Harvard Corporation)
• Inventors: Ghassemzadeh, Saeed S., Ji, Lusheng, Miller, Robert Raymond II, Gupta, Manish, Tarokh, Vahid
• Primary Examiner(s): Hill, Stanley K
• Assistant Examiner(s): BUSS, BENJAMIN J
• Application Number:
  US14/099,570
• Publication Number:
  US 20150157274A1
• Time in Patent Office: 1,019 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: A43B 3/34 with electrical or electronic arrangements
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 2562/0247 Pressure sensors
• 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 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/1117 Fall detection
• 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 5/1126 using a particular sensing technique
• CPC Class Codes: A61B 5/4082 Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
• CPC Class Codes: A61B 5/4878 Evaluating oedema
• CPC Class Codes: A61B 5/6802 Sensor mounted on worn items
• CPC Class Codes: A61B 5/6829 Foot or ankle
• CPC Class Codes: A61B 5/7267 involving training the classification device
• 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: A61B 5/7405 using sound
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 70/60 relating to pathologies

Abstract

A method, computer-readable storage device and apparatus for determining a regression of a medical condition are disclosed. For example, a method receives characteristics of motion information, wherein the characteristics of motion information is based upon gait information, compares the characteristics of motion information over a time period to a profile of the medical condition, wherein the profile of the medical condition comprises a plurality of signatures associated with different stages of the medical condition, determines a potential presence of the regression of the medical condition when the characteristics of motion information matches one of the plurality of signatures, and transmits a notification of the potential presence of the regression of the medical condition.

Claims

1. A method for determining a regression of a medical condition, the method comprising:

receiving, by a processor deployed in a communication network, characteristics of motion information, wherein the characteristics of motion information is based upon gait information;

comparing, by the processor, the characteristics of motion information over a time period to a profile of the medical condition, wherein the profile of the medical condition comprises a plurality of signatures associated with different stages of the medical condition, wherein each of the plurality of signatures comprises a hidden markov model, wherein each of the plurality of signatures is a signature of a mode of motion that is associated with one of the different stages of the medical condition;

determining, by the processor, a potential presence of the regression of the medical condition when the characteristics of motion information matches at least two of the plurality of signatures; and

transmitting, by the processor, a notification of the potential presence of the regression of the medical condition.

2. The method of claim 1, wherein the gait information comprises pressure information.

3. The method of claim 2, wherein the gait information further comprises at least one of:

acceleration information or gyroscopic information.

4. The method of claim 1, wherein the transmitting the notification comprises directing an action to be taken.

5. The method of claim 4, wherein the gait information further comprises location information, wherein the action is based upon the location information.

6. The method of claim 1, wherein the characteristics of motion information comprises one of:

a stride length, a speed, an acceleration, an elevation, or a mode of motion.

7. The method of claim 1, wherein the medical condition comprises a degenerative disease.

8. The method of claim 7 wherein the degenerative disease comprises one of:

Parkinson'"'"'s disease, Huntington disease, Alzheimer disease, adrenoleukodystrophy, multiple sclerosis, arthritis or dementia.

9. The method of claim 1, wherein the gait information is continuously received and wherein the characteristics of motion information is based upon a temporal sequence of the gait information.

10. The method of claim 1, further comprising:

receiving the gait information from a set of wearable devices.

11. The method of claim 10, wherein the set of wearable devices comprises a pair of foot-mounted sensors.

12. The method of claim 1, wherein the gait information is received via a network comprising one of:

a cellular access network or a ZigBee mesh network.

13. The method of claim 12, wherein a set of wearable devices is for gathering the gait information and for communicating the gait information to one of:

the cellular access network or the ZigBee mesh network.

14. The method of claim 13, wherein the set of wearable devices is for communicating the gait information to the ZigBee mesh network when the set of wearable devices is within a communication range of the ZigBee mesh network and wherein the set of wearable devices is for communicating the gait information to the cellular access network when the set of wearable devices is not within the communication range of the ZigBee mesh network.

15. The method of claim 13, wherein the set of wearable devices is for communicating the gait information to the cellular access network via an intermediary cellular endpoint device.

16. The method of claim 1, wherein the gait information is time stamped prior to the gait information being received by the processor.

17. A computer-readable storage device storing instructions which, when executed by a processor deployed in a communication network, cause the processor to perform operations for determining a regression of a medical condition, the operations comprising:

receiving characteristics of motion information, wherein the characteristics of motion information is based upon gait information;

comparing the characteristics of motion information over a time period to a profile of the medical condition, wherein the profile of the medical condition comprises a plurality of signatures associated with different stages of the medical condition, wherein each of the plurality of signatures comprises a hidden markov model, wherein each of the plurality of signatures is a signature of a mode of motion that is associated with one of the different stages of the medical condition;

determining a potential presence of the regression of the medical condition when the characteristics of motion information matches at least two of the plurality of signatures; and

transmitting a notification of the potential presence of the regression of the medical condition.

18. The computer-readable storage device of claim 17, wherein the gait information comprises pressure information.

19. The computer-readable storage device of claim 18, wherein the gait information further comprises at least one of:

acceleration information or gyroscopic information.

20. An apparatus for determining a regression of a medical condition, the apparatus comprising:

a processor deployed in a communication network; and

a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations, the operations comprising;

receiving characteristics of motion information, wherein the characteristics of motion information is based upon gait information;

comparing the characteristics of motion information over a time period to a profile of the medical condition, wherein the profile of the medical condition comprises a plurality of signatures associated with different stages of the medical condition, wherein each of the plurality of signatures comprises a hidden markov model, wherein each of the plurality of signatures is a signature of a mode of motion that is associated with one of the different stages of the medical condition;

determining a potential presence of the regression of the medical condition when the characteristics of motion information matches at least two of the plurality of signatures; and

transmitting a notification of the potential presence of the regression of the medical condition.