WIRELESS SENSOR BASED QUANTITATIVE FALLS RISK ASSESSMENT

• US 20140350884A1
• Filed: 08/08/2014
• Published: 11/27/2014
• 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 9,427,178 B2
• Time in Patent Office: Days
• US Class Current: 702/145
• 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

1-20. -20. (canceled)

21. A system comprising:

a plurality of shank-mounted kinematic sensors;

a processor; and

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

obtain a plurality of adaptive thresholds whose values change based on angular velocity data 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.

22. The system of claim 21, wherein the instructions, when executed further cause the system to obtain 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.

23. The system of claim 22, wherein the instructions, when executed further cause the system to obtain 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.

24. The system of claim 23, wherein the instructions, when executed further cause the system to generate a falls risk assessment based on at least one of the first parameter and the derived parameter.

25. The system of claim 23, wherein the instructions, when executed, further cause the system to multiply the set of tri-axial angular velocities by a height of the individual to obtain the set of tri-axial linear velocities.

26. The system of claim 21, the instructions, when executed, further cause the system to reject a heel-strike point or a toe-off point as spurious based on temporal sequence information or gait cycle information.

27. The system of claim 21, wherein the instructions, when executed, further cause the system 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.

28. The system of claim 27, wherein the instructions, when executed further cause the system to change a falls risk assessment based on a change in a value of the interaction parameter.

29. A method comprising:

obtaining a plurality of adaptive thresholds whose values change based on angular velocity data from a 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.

30. The method of claim 29, further comprising obtaining 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.

31. The method of claim 30, further comprising obtaining 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.

32. The method of claim 31, further comprising providing, to a user, a falls risk assessment based on at least one of the first parameter and the derived parameter.

33. The method of claim 31, further comprising multiplying the set of tri-axial angular velocities by a height of the individual to obtain the set of tri-axial linear velocities.

34. The method of claim 29, further comprising rejecting a heel-strike point or a toe-off point as spurious based on temporal sequence information or gait cycle information.

35. The method of claim 29, further comprising obtaining 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.

36. The method of claim 35, further comprising changing a falls risk assessment based on a change in a value of the interaction parameter.

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

obtain a plurality of adaptive thresholds whose values change based on angular velocity data from a 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.

38. The non-transitory computer readable storage medium of claim 37, wherein the instructions, when executed, further cause the system to obtain a first parameter based on a timed up and go (TUG) time segment.

39. The non-transitory computer readable storage medium of claim 38, wherein the instructions, when executed, further cause the system 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.

40. The non-transitory computer readable storage medium of claim 37, wherein the instructions, when executed, further cause the system 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.

41. The non-transitory computer readable storage medium of claim 40, wherein the instructions, when executed further cause the system to change a falls risk assessment based on a change in a value of the interaction parameter.