METHOD AND APPARATUS FOR USING GAIT ANALYSIS TO DETERMINE A HEALTH QUALITY MEASURE

• US 20150161511A1
• Filed: 12/06/2013
• Published: 06/11/2015
• 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
• Granted Patent: US 9,443,063 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• 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 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A method, computer-readable storage device and apparatus for calculating a health quality measure are disclosed. For example, a method receives characteristics of motion information, wherein the characteristics of motion information is based upon gait information, monitors the characteristics of motion information over a time period to determine a plurality of different modes of motion within the time period, and calculates the health quality measure based upon the plurality of different modes of motion.

Claims

1. A method for calculating a health quality measure, 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;

monitoring, by the processor, the characteristics of motion information over a time period to determine a plurality of different modes of motion within the time period; and

calculating, by the processor, the health quality measure based upon the plurality of different modes of motion.

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

3. The method of claim 1, wherein each of the plurality of different modes of motion comprises one of:

running, walking, falling, jumping, standing, cycling and sitting.

4. The method of claim 1, wherein the plurality of different modes of motion is included in the characteristics of motion information.

5. The method of claim 4, wherein the characteristics of motion information further comprise at least one of:

a stride length, a speed, an acceleration, and an elevation.

6. The method of claim 1, wherein the health quality measure comprises one of:

a number of calories burned, a time spent performing a particular type of activity during the time period, a distance traveled, an average speed, a score comparing a current level of activity to a previous level of activity, and a number of medical episodes during the time period.

7. The method of claim 1, further comprising:

receiving an activity profile that specifies a target health quality level.

8. The method of claim 7, further comprising:

comparing the health quality measure to the target health quality level specified in the activity profile; and

transmitting a notification indicating whether the target health quality level has been satisfied.

9. The method of claim 7, wherein the activity profile is determined by a medical provider.

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 characteristics of motion information is based upon the gait information.

12. The method of claim 11, wherein the characteristics of motion information is determined based upon the gait information by comparing the gait information that is received to a plurality of signatures of mode of motion.

13. The method of claim 12, wherein each of the plurality of signatures of mode of motion comprises a hidden markov model.

14. The method of claim 1, wherein the gait information is received via one of:

a cellular access network and a ZigBee mesh network.

15. The method of claim 14, 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 and the ZigBee mesh network.

16. The method of claim 15, wherein the set of wearable devices communicates 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 communicates the gait information to the cellular access network when the set of wearable devices is not within a communication range of the ZigBee mesh network.

17. The method of claim 15, wherein the set of wearable devices communicates the gait information to the cellular access network via an intermediary cellular endpoint device.

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

19. 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 calculating a health quality measure, the operations comprising:

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

monitoring the characteristics of motion information over a time period to determine a plurality of different modes of motion within the time period; and

calculating the health quality measure based upon the plurality of different modes of motion.

20. An apparatus comprising:

a processor deployed in a communication network; and

a computer-readable storage device 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;

monitoring the characteristics of motion information over a time period to determine a plurality of different modes of motion within the time period; and

calculating a health quality measure based upon the plurality of different modes of motion.