FALL PREVENTION

• US 20120095722A1
• Filed: 07/06/2010
• Published: 04/19/2012
• Est. Priority Date: 07/10/2009
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Ten Kate, Warner Rudolph Theophile
• Application Number:
  US13/377,419
• Publication Number:
  US 20120095722A1
• Time in Patent Office: Days
• US Class Current: 702/141
• CPC Class Codes: A61B 5/1117 Fall detection
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: G01C 22/006 Pedometers
• CPC Class Codes: G01P 15/00 Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
• CPC Class Codes: G08B 21/0446 worn on the body to detect changes of posture, e.g. a fall, inclination, acceleration, gait
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

There is provided a method of determining a fall risk of a user, the method comprising collecting measurements of the motion of the user, estimating a value for a parameter related to the gait of the user from the measurements, and determining a fall risk for the user from a comparison of the estimated value with a normal value for the parameter determined from motion of the user in which the user is at their normal risk of falling.

Claims

1. A method of determining a fall risk of a user, the method comprising:

collecting measurements of the motion of the user;

estimating a value for a parameter related to the gait of the user from the measurements; and

determining a fall risk for the user from a comparison of the estimated value with a normal value for the parameter determined from motion of the user in which the user is at their normal risk of falling.

2. A method as claimed in claim 1, wherein the step of determining a fall risk comprises weighting the comparison between the estimated value and the normal value according to a standard deviation of the normal value.

3. A method as claimed in claim 1, wherein the estimated value is determined from motion of the user over a period of time that is shorter than the period of time over which the normal value is determined.

4. A method as claimed in claim 1, wherein the step of estimating comprises identifying a step boundary in the collected measurements.

5. A method as claimed in claim 4, wherein the step of identifying a step boundary comprises identifying clusters of contiguous measurements in the collected measurements in which the magnitude of each of the measurements exceeds a threshold.

6. A method as claimed in claim 4, wherein the step of identifying a step boundary comprises identifying clusters of contiguous measurements in the collected measurements in which the magnitude of each of the measurements exceeds a threshold, apart from a subset of the measurements whose magnitude is less than the threshold, provided that the subset covers a time period less than a time threshold.

7. A method as claimed in claim 4, wherein the step of identifying a step boundary comprises identifying clusters of contiguous measurements in the collected measurements, wherein the first collected measurement in the collected measurements whose magnitude exceeds a first threshold denotes the first measurement in a cluster and wherein the first collected measurement after the first measurement in the cluster whose magnitude falls below a second threshold denotes the last measurement in the cluster, provided that the last measurement is more than a minimum period after the first measurement.

8. A method as claimed in claim 5, wherein the step of identifying step boundaries further comprises identifying the step boundary as the measurement in each cluster with the highest magnitude.

9. A method as claimed in claim 4, wherein the parameter related to the gait of the user comprises a step size and the step of estimating a value for the parameter comprises integrating horizontal components of the collected measurements with the integral bounds being given by consecutive identified step boundaries.

10. A method as claimed in claim 9, wherein the step of estimating a value for the parameter comprises computing a double integration with respect to time of the horizontal components of the collected measurements relating to acceleration, the integration constants being set to zero at the beginning of the step.

11. A method as claimed in claim 4, wherein the parameter related to the gait of the user comprises, or additionally comprises, a forward step size and the step of estimating a value for the parameter comprises:

integrating horizontal components of the collected measurements with the integral bounds being given by consecutive identified step boundaries to give a start and end position for a step; and

determining the forward step size as the norm of the vector connecting the start and end positions.

12. A method as claimed in claim 10, wherein the parameter related to the gait of the user additionally comprises a lateral step size and the step of estimating a value for the parameter further comprises:

defining a straight line between the start and end positions;

integrating collected measurements occurring during the step to give a series of positions during the step;

determining the distance between each position and the straight line; and

determining the lateral step size as the maximum distance in this series.

13. A method as claimed in claim 1, further comprising a calibration step that includes:

collecting measurements of the motion of the user when the user is at their normal risk of falling; and

estimating the normal value for the parameter related to the gait of the user from the collected measurements.

15. A computer program product comprising computer-readable code that, when executed on a suitable computer or processor, is configured to cause the computer or processor to perform the steps in the method defined in claim 1.

14. A fall prevention device, comprising:

at least one sensor for collecting measurements of the motion of a user of the device; and

a processor for estimating a value for a parameter related to the gait of the user from the measurements, and for determining a fall risk for the user from a comparison of the estimated value with a value of the parameter determined from motion of the user in which the user is at their normal risk of falling.