Determining postural stability

• US 8,011,229 B2
• Filed: 11/26/2008
• Issued: 09/06/2011
• Est. Priority Date: 11/28/2007
• Status: Active Grant

• Current Assignee: President and Fellows of Harvard College (Harvard Corporation), Massachusetts Institute of Technology, United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration
• Original Assignee: President and Fellows of Harvard College (Harvard Corporation), Massachusetts Institute of Technology
• Inventors: Paloski, William H., Forth, Katharine E., Lieberman, Erez
• Primary Examiner(s): NOORI, MASOUD H
• Application Number:
  US12/323,912
• Publication Number:
  US 20090260426A1
• Time in Patent Office: 1,014 Days
• Field of Search: 73 6501- 6509
• US Class Current: 73/65.01
• CPC Class Codes: A61B 2503/40 Animals
• CPC Class Codes: A61B 2562/0247 Pressure sensors
• CPC Class Codes: A61B 2562/046 in a matrix array
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/1036 Measuring load distribution, e.g. podologic studies
• CPC Class Codes: A61B 5/1038 Measuring plantar pressure during gait
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/1117 Fall detection
• CPC Class Codes: A61B 5/1118 Determining activity level
• 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 5/4023 Evaluating sense of balance
• CPC Class Codes: A61B 5/6807 Footwear
• CPC Class Codes: A61B 5/6829 Foot or ankle
• CPC Class Codes: G01G 19/44 for weighing persons
• CPC Class Codes: G01G 23/3735 using a digital network
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A method for determining postural stability of a person can include acquiring a plurality of pressure data points over a period of time from at least one pressure sensor. The method can also include the step of identifying a postural state for each pressure data point to generate a plurality of postural states. The method can include the step of determining a postural state of the person at a point in time based on at least the plurality of postural states.

Claims

1. A computer-implemented method for determining postural stability of a person, the method comprising:

acquiring, by a processor, a plurality of pressure data points over a period of time from at least one pressure sensor;

identifying, by the processor, a postural state for each pressure data point to generate a plurality of postural states; and

determining, by the processor, a postural state of the person at a point in time based on at least the plurality of postural states and a probability of transitioning between at least one of the plurality of postural states and another postural state, wherein the postural state is at least one of a static postural state or a dynamic postural state.

2. The method of claim 1, wherein the person is in the dynamic postural state when moving from a first static postural state to a second static postural state.

3. The method of claim 1, further comprising determining that the person is posturally stable or posturally unstable based on a number of times the person is in the dynamic postural state.

4. The method of claim 1, wherein the plurality of postural states follows a punctuated equilibrium, a continuous series of static postural states defining an equilibrium.

5. The method of claim 4, further comprising determining that the person is posturally stable or posturally unstable based on a number of distinct equilibria.

6. The method of claim 1, wherein determining the postural state of the person at the point in time is based on at least a probability of transitioning between the static postural state and the dynamic postural state.

7. The method of claim 6, further comprising calculating the probability of transitioning between the static postural state and the dynamic postural state based on the plurality of postural states of the person over the period of time.

8. The method of claim 1, further comprising acquiring at least one of an acceleration or a location of the person over the period of time.

9. The method of claim 1, further comprising determining a range of postural stability states based on the plurality of pressure data points.

10. The method of claim 1, wherein determining comprises using a machine learning technique on the plurality of pressure data points to generate the plurality of postural states.

11. The method of claim 10, wherein determining comprises using a Hidden Markov Model on the plurality of pressure data points or applying Bayesian segmentation to the plurality of pressure data points to generate the plurality of postural states.

12. The method of claim 1, wherein acquiring comprises acquiring a plurality of pressure data points from at least one pressure sensor on at least one of a shoe, a sock, a sole insert, a cane, a crutch, a walker, a walking aid, a prosthetic leg, or a robotic leg.

13. The method of claim 1, wherein each of the plurality of pressure data points reflects a location of a center of mass of the person at a point in time.

14. The method of claim 13, wherein identifying comprises grouping a selected set of the plurality of pressure data points as corresponding to a static postural state based on the location of the center of mass of the person of each of the plurality of pressure data points.

15. The method of claim 1, wherein determining comprises determining in real time, a subsequent postural state of the person based on the plurality of pressure data points.

16. The method of claim 1, wherein determining comprises determining a current postural state or a subsequent postural state of the person based on the plurality of pressure data points, wherein the plurality of pressure data points were acquired during a previous period in time.

17. A computer-implemented method for determining postural stability, the method comprising:

acquiring, by a processor, at least a first pressure data point and a second pressure data point from at least one pressure sensor;

identifying, by the processor, a first postural state and a second postural state based on the first and second pressure data points, wherein identifying comprises identifying the first postural state as static or dynamic and the second postural state as static or dynamic; and

determining, by the processor, a postural state at a point in time based on at least the first postural state, the second postural state, and a probability of transitioning between at least one of the first or second postural states and another postural state.

18. The method of claim 17, wherein determining comprises determining a subsequent postural state of a person.

19. The method of claim 17, wherein determining comprises determining a subsequent postural state of a person based on a probability of transitioning between the static postural state and the dynamic postural state.

20. The method of claim 17, further comprising determining that a person is posturally stable or posturally unstable based on at least the identified first postural state and the identified second postural state.

21. The method of claim 17, wherein acquiring comprises acquiring a plurality of pressure data points from at least one pressure sensor on at least one of a shoe, a sock, a sole insert, a cane, a crutch, a walker, a walking aid, a prosthetic leg, a robotic leg, a vehicle, or an axle connected to at least one wheel.

22. A system for determining postural stability of a person comprising:

at least one pressure sensor coupled to the person that acquires a plurality of pressure data points over a period of time;

means for identifying a postural state for each pressure data point;

means for generating a plurality of postural states of the person over the period of time; and

means for determining a postural state of the person at a point in time based on the plurality of postural states and a probability of transitioning between at least one of the plurality of postural states and another postural state, wherein the postural state of the person is one of a static postural state or a dynamic postural state.

23. The system of claim 22, wherein each pressure data point corresponds to a center of gravity of the person at a point in time.