Wireless sensor based quantitative falls risk assessment

• US 9,427,178 B2
• Filed: 08/08/2014
• Issued: 08/30/2016
• Est. Priority Date: 05/18/2010
• Status: Active Grant

• Current Assignee: Linus Health Europe Limited
• Original Assignee: Care Innovations LLC
• Inventors: Greene, Barry R.
• Primary Examiner(s): Lee, Paul D
• Application Number:
  US14/455,032
• Publication Number:
  US 20140350884A1
• Time in Patent Office: 753 Days
• Field of Search: 702/145
• US Class Current: 1/1
• 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 system comprising:

a plurality of shank-mounted kinematic sensors for coupling to an individual that are configured to measure tri-axial angular velocity data while the individual is walking;

a processor configured to receive the measured tri-axial angular velocity data and for obtaining a plurality of adaptive thresholds based on the measured tri-axial angular velocity data, wherein values of the plurality of adaptive thresholds are configured to change based on the measured tri-axial angular velocity data;

a user interface device configured to allow a user to interact with and perceive information from the system, and

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

start obtaining at the processor measured tri-axial angular velocity data during a period of time using the plurality of shank-mounted kinematic sensors coupled to the individual while the individual is walking and, at the end of the period of time, stop obtaining at the processor measured tri-axial angular velocity data using the plurality of shank-mounted kinematic sensors;

using the processor, obtain the plurality of adaptive thresholds based on the tri-axial angular velocity data received from the plurality of kinematic sensors, wherein the plurality of adaptive thresholds defines a likely range of a plurality of heel-strikes and toe-off points in the angular velocity data;

determine, using the processor, the plurality of heel-strikes and toe-off points based on the plurality of adaptive thresholds;

generate, using the processor, a falls risk assessment used to indicate a risk of falling, the falls risk assessment being based on at least the period of time, the measured tri-axial angular velocity data, and the plurality of heel-strikes and toe-off points; and

provide to a user via the user interface device at least the generated falls risk assessment for user interaction therewith.

2. The system of claim 1, wherein the instructions, when executed further cause the system to obtain, using the processor, a first parameter based on a timed up and go (TUG) time segment, wherein the TUG time segment comprises at least one of a walk time, a turn time, and a return time.

3. The system of claim 2, wherein the instructions, when executed further cause the system to obtain, using the processor, a derived parameter based on the angular velocity data, wherein the derived parameter includes at least one of:

a temporal gait parameter or an angular-velocity parameter, wherein the temporal gait parameter includes at least one of a cadence, a walk-time ratio, a swing time, a step time, a number of gait cycles, a single support percentage, a double support percentage, or 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, or a mid-swing point angular velocity.

4. The system of claim 3, wherein the generation of the falls risk assessment is based on at least one of the first parameter and the derived parameter.

5. The system of claim 3, wherein the instructions, when executed, further cause the system to multiply, using the processor, the set of tri-axial angular velocities by a height of the individual to obtain the set of tri-axial linear velocities.

6. The system of claim 1, the instructions, when executed, further cause the system to reject, using the processor, a heel-strike point or a toe-off point as spurious based on temporal sequence information or gait cycle information.

7. The system of claim 1, wherein the instructions, when executed, further cause the system to obtain, using the processor, an interaction parameter that measures a two-way interaction between single support percentage variability and step time variability, between a walk-time ratio and height, between mean vertical angular velocity and max vertical angular velocity, between age of an individual and swing time variability, between return time and total TUG time, between mean angular velocity and weight of the individual, between range of mid-swing points and cadence, or between a number of gait cycles and turn angular velocity.

8. The system of claim 7, wherein the instructions, when executed further cause the system to change, using the processor, a falls risk assessment based on a change in a value of the interaction parameter.

9. A method comprising:

providing a plurality of shank-mounted kinematic sensors on an individual, the sensors being configured to measure tri-axial angular velocity data while the individual is walking;

measuring tri-axial angular velocity data during a period of time using the plurality of shank-mounted kinematic sensors, the angular velocity data being measured while the individual is walking;

obtaining, at a processor, the measured tri-axial angular velocity data during the period of time and obtaining a plurality of adaptive thresholds based on receiving the measured tri-axial angular velocity data, wherein values of the plurality of adaptive thresholds change based on the angular velocity data received from the plurality of shank-mounted kinematic sensors, wherein the plurality of adaptive thresholds defines a likely range of a plurality of heel-strikes and toe-off points in the angular velocity data;

determining, using the processor, the plurality of heel-strikes and toe-off points based on the plurality of adaptive thresholds; and

generating, using the processor, a falls risk assessment used to indicate a risk of falling, the falls risk assessment being based on at least the period of time, the measured tri-axial angular velocity data, and the plurality of heel-strikes and toe-off points; and

providing to a user via a user interface device at least the generated falls risk assessment for user interaction therewith.

10. The method of claim 9, further comprising obtaining, using the processor, a first parameter based on a timed up and go (TUG) time segment, wherein the TUG time segment comprises at least one of a walk time, a turn time, and a return time.

11. The method of claim 10, further comprising obtaining, using the processor, a derived parameter based on the angular velocity data, wherein the derived parameter includes at least one of a temporal gait parameter or an angular-velocity parameter, wherein the temporal gait parameter includes at least one of a cadence, a walk-time ratio, a swing time, a step time, a number of gait cycles, a single support percentage, a double support percentage, or 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, or a mid-swing point angular velocity.

12. The method of claim 11, further comprising providing, to the user, the generated falls risk assessment based on at least one of the first parameter and the derived parameter.

13. The method of claim 11, further comprising multiplying, using the processor, the set of tri-axial angular velocities by a height of the individual to obtain the set of tri-axial linear velocities.

14. The method of claim 9, further comprising rejecting, using the processor, a heel-strike point or a toe-off point as spurious based on temporal sequence information or gait cycle information.

15. The method of claim 9, further comprising obtaining, using the processor, an interaction parameter that measures a two-way interaction between single support percentage variability and step time variability, between a walk-time ratio and height, between mean vertical angular velocity and max vertical angular velocity, between age of an individual and swing time variability, between return time and total TUG time, between mean angular velocity and weight of the individual, between range of mid-swing points and cadence, or between a number of gait cycles and turn angular velocity.

16. The method of claim 15, further comprising changing, using the processor, a falls risk assessment based on a change in a value of the interaction parameter.

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

start obtaining at the processor measured tri-axial angular velocity data during a period of time using the plurality of shank-mounted kinematic sensors coupled to the individual while the individual is walking and, at the end of the period of time, stop obtaining at the processor measured tri-axial angular velocity data using the plurality of shank-mounted kinematic sensors;

using the processor, obtain the plurality of adaptive thresholds based on the tri-axial angular velocity data received from the plurality of kinematic sensors, wherein the plurality of adaptive thresholds defines a likely range of a plurality of heel-strikes and toe-off points in the angular velocity data;

determine, using the processor, the plurality of heel-strikes and toe-off points based on the plurality of adaptive thresholds;

generate, using the processor, a falls risk assessment used to indicate a risk of falling, the falls risk assessment being based on at least the period of time, the measured tri-axial angular velocity data, and the plurality of heel-strikes and toe-off points; and

provide to a user via the user interface device at least the generated falls risk assessment for user interaction therewith.

18. The non-transitory computer readable storage medium of claim 17, wherein the instructions, when executed, further cause the processor to obtain a first parameter based on a timed up and go (TUG) time segment.

19. The non-transitory computer readable storage medium of claim 18, wherein the instructions, when executed, further cause the processor to obtain a derived parameter based on the angular velocity data, and generate a falls risk assessment based on at least one of the first parameter and the derived parameter.

20. The non-transitory computer readable storage medium of claim 17, wherein the instructions, when executed, further cause the processor to obtain an interaction parameter that measures a two-way interaction between single support percentage variability and step time variability, between a walk-time ratio and height, between mean vertical angular velocity and max vertical angular velocity, between age of an individual and swing time variability, between return time and total TUG time, between mean angular velocity and weight of the individual, between range of mid-swing points and cadence, or between a number of gait cycles and turn angular velocity.

21. The non-transitory computer readable storage medium of claim 20, wherein the instructions, when executed further cause the processor to change a falls risk assessment based on a change in a value of the interaction parameter.