WIRELESS SENSOR BASED QUANTITATIVE FALLS RISK ASSESSMENT

• US 20110288811A1
• Filed: 05/18/2010
• Published: 11/24/2011
• Est. Priority Date: 05/18/2010
• Status: Active Grant

• Current Assignee: Linus Health Europe Limited
• Original Assignee: Intel-GE Care Innovations, LLC (Intel Corporation)
• Inventors: Greene, Barry R.
• Granted Patent: US 8,805,641 B2
• Time in Patent Office: Days
• US Class Current: 702/141
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/1038 Measuring plantar pressure during gait
• 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 5/6828 Leg
• 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: G01P 15/00 Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
• CPC Class Codes: G01P 3/00 Measuring linear or angular speed; Measuring differences of linear or angular speeds G01P5/00 - G01P11/00 take precedence; direction and speed indication G01P13/045; counting mechanisms G06M
• CPC Class Codes: G01P 3/64 Devices characterised by the determination of the time taken to traverse a fixed distance
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

Methods and systems may provide for a plurality of kinematic sensors to be coupled to a corresponding plurality of shanks of an individual, a processor, and a memory to store a set of instructions. If executed by the processor, the instructions can cause the system to calculate a timed up and go (TUG) time segment based on angular velocity data from the plurality of kinematic sensors. The instructions may also cause the system to calculate a derived parameter based on the angular velocity data, and generate a falls risk assessment based on at least one of the TUG time segment and the derived parameter.

Claims

1. A method comprising:

calculating a plurality of adaptive thresholds based on angular velocity data from a plurality of shank-mounted kinematic sensors;

detecting a plurality of heel-strike points and toe-off points based on the angular velocity data;

calculating a timed up and go (TUG) time segment based on the plurality of heel-strike points and toe-off points, wherein the TUG time segment is at least one of a walk time, a turn time, and a return time, the walk time identifies an amount of time between a first step and a last step of a TUG test, the turn time identifies an amount of time between the first step and a turn step of the TUG test, and the return time identifies an amount of time between the turn step and the last step of the TUG test;

calculating a derived parameter based on the angular velocity data, wherein the derived parameter includes at least one of a temporal gait parameter and an angular velocity-based parameter;

rejecting at least one of a spurious heel-strike point and a spurious toe-off point; and

generating a falls risk assessment based on at least one of the TUG time segment and the derived parameter.

2. The method of claim 1, wherein the first step is defined by at least one of a first heel-strike point and a first toe-off point, the turn step is defined by at least one of a turn heel-strike point and a turn toe-off point, and the last step is defined by at least one of a last heel-strike point and a last toe-off point, the method further including detecting the heel-strike points and toe-off points based on the angular velocity data.

3. The method of claim 1, wherein the temporal gait parameter includes at least one of a cadence, a number of gait cycles and a number of steps taken, and the angular velocity-based parameter includes at least one of a set of tri-axial angular velocities, a set of tri-axial linear velocities, and a mid-swing point angular velocity.

4. The method of claim 3, further including multiplying the set of tri-axial angular velocities by a height of an individual to obtain the set of tri-axial linear velocities.

5. A system comprising:

a plurality of kinematic sensors to be coupled to a corresponding plurality of shanks of an individual;

a processor; and

a memory to store a set of instructions which, if executed by the processor, cause the system to,

calculate a timed up and go (TUG) time segment based on angular velocity data from the plurality of kinematic sensors;

calculate a derived parameter based on the angular velocity data; and

generate a falls risk assessment based on at least one of the TUG time segment and the derived parameter.

6. The system of claim 5, wherein the TUG time segment is to be at least one of a walk time, a turn time, and a return time, wherein the walk time is to identify an amount of time between a first step and a last step of a TUG test, the turn time is to identify an amount of time between the first step and a turn step of the TUG test, and the return time is to identify an amount of time between the turn step and the last step of the TUG test.

7. The system of claim 6, wherein the first step is to be defined by at least one of a first heel-strike point and a first toe-off point, the turn step is to be defined by at least one of a turn heel-strike point and a turn toe-off point, and the last step is to be defined by at least one of a last heel-strike point and a last toe-off point, and the instructions, if executed, are to cause the system to detect the heel-strike points and toe-off points based on the angular velocity data.

8. The system of claim 7, wherein the instructions, if executed, further cause the system to:

calculate a plurality of adaptive thresholds based on the angular velocity data;

detect the heel-strike and toe-off points based on the plurality of adaptive thresholds; and

reject at least one of a spurious heel-strike point and a spurious toe-off point.

9. The system of claim 5, wherein the derived parameter is to include at least one of a temporal gait parameter and an angular velocity parameter.

10. The system of claim 9, wherein the temporal gait parameter is to include at least one of a cadence, a number of gait cycles and a number of steps taken.

11. The system of claim 9, wherein the angular velocity-based parameter is to include at least one of a set of tri-axial angular velocities, a set of tri-axial linear velocities, and a mid-swing point angular velocity.

12. The system of claim 11, wherein the instructions, if executed, further cause a computer to multiply the set of tri-axial angular velocities by a height of the individual to obtain the set of tri-axial linear velocities.

13. A computer readable storage medium comprising a set of instructions which, if executed by a processor, cause a computer to:

calculate a timed up and go (TUG) time segment based on angular velocity data from a plurality of shank-mounted kinematic sensors;

calculate a derived parameter based on the angular velocity data; and

generate a falls risk assessment based on at least one of the TUG time segment and the derived parameter.

14. The medium of claim 13, wherein the TUG time segment is to be at least one of a walk time, a turn time, and a return time, wherein the walk time is to identify an amount of time between a first step and a last step of a TUG test, the turn time is to identify an amount of time between the first step and a turn step of the TUG test, and the return time is to identify an amount of time between the turn step and the last step of the TUG test.

15. The medium of claim 14, wherein the first step is to be defined by at least one of a first heel-strike point and a first toe-off point, the turn step is to be defined by at least one of a turn heel-strike point and a turn toe-off point, and the last step is to be defined by at least one of a last heel-strike point and a last toe-off point, and the instructions, if executed, are to cause a computer to detect the heel-strike points and toe-off points based on the angular velocity data.

16. The medium of claim 15, wherein the instructions, if executed, further cause a computer to:

calculate a plurality of adaptive thresholds based on the angular velocity data;

detect the heel-strike and toe-off points based on the plurality of adaptive thresholds; and

reject at least one of a spurious heel-strike point and a spurious toe-off point.

17. The medium of claim 13, wherein the derived parameter is to include at least one of a temporal gait parameter and an angular velocity-based parameter.

18. The medium of claim 17, wherein the temporal gait parameter is to include at least one of a cadence, a number of gait cycles and a number of steps taken.

19. The medium of claim 17, wherein the angular velocity-based parameter is to include at least one of a set of tri-axial angular velocities, a set of tri-axial linear velocities, and a mid-swing point angular velocity.

20. The medium of claim 19, wherein the instructions, if executed, further cause a computer to multiply the set of tri-axial angular velocities by a height of an individual to obtain the set of tri-axial linear velocities.