Calculation of minimum ground clearance using body worn sensors

• US 9,524,424 B2
• Filed: 09/01/2011
• Issued: 12/20/2016
• Est. Priority Date: 09/01/2011
• Status: Active Grant

• Current Assignee: Linus Health Europe Limited
• Original Assignee: Care Innovations LLC
• Inventors: Greene, Barry R.
• Primary Examiner(s): Toatley, Gregory J
• Assistant Examiner(s): Quigley, Kyle R
• Application Number:
  US13/223,759
• Publication Number:
  US 20130060512A1
• Time in Patent Office: 1,937 Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/1036 Measuring load distribution, e.g. podologic studies
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/1121 Determining geometric values, e.g. centre of rotation or angular range of movement
• CPC Class Codes: A61B 5/1127 using markers
• CPC Class Codes: A61B 5/7285 for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal A61B6/5205, A61B6/541, A61B8/5207, A61B8/543 take precedence
• CPC Class Codes: G06F 2218/20 by applying autoregressive analysis
• CPC Class Codes: G06V 40/23 Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

Methods, systems, and apparatus for deriving a relationship between minimum ground clearance (MGC) and inertial sensor data. A regression model may be generated by collecting tri-axial angular velocity and acceleration data from inertial sensors and MGC data from optical motion capture systems during a walking trial. A linear, quadratic, interaction, stepwise interaction, or another regression model may be generated. The regression model may estimate the MGC as a function of one or more parameters measured by or derived from the inertial sensor data. The regression model may be used to calculate an estimate of the MGC based on inertial sensor data collected from one or more individuals.

Claims

1. A method for assessing falls risk in one or more persons using a system comprising one or more inertial sensors and at least one processor, the method comprising:

collecting reference data to generate a regression model using the at least one processor, and wherein the collecting of reference data comprises;

measuring inertial sensor trial data using one or more inertial sensors on one or more persons during a walking trial; and

measuring reference minimum ground clearance measurements associated with the one or more persons using an optical motion capture system having at least one camera during the walking trial;

generating, by the at least one processor, the regression model using;

(a) the measured inertial sensor trial data from one or more inertial sensors associated with one or more persons and (b) the measured reference minimum ground clearance measurements from the optical capture system associated with the one or more persons collected as the reference data during the walking trial, wherein the generated regression model represents minimum ground clearance parameters as a function of one or more parameters of the motion data from the one or more inertial sensors;

after generating the regression model,

measuring subsequent motion data using one or more inertial sensors on a first person while walking;

calculating, by the at least one processor, a minimum ground clearance parameter of the first person using the generated regression model, wherein an input to the generated regression model comprises parameters selected from the measured subsequent motion data of the first person, and wherein the calculation is performed without use of data from the optical motion capture system; and

generating, using the processor, a value indicative of a risk of fall for the first person based on the calculated minimum ground clearance parameter of the first person.

2. The method of claim 1, wherein the minimum ground clearance parameter comprises a mean minimum ground clearance, a coefficient of variation of minimum ground clearance, or any combination thereof.

3. The method of claim 1, wherein the one or more inertial sensors comprise an accelerometer and a gyroscope.

4. The method of claim 1, further comprising one or more foot-mounted inertial sensors for measuring the measured inertial sensor trial data and the measured subsequent motion data of the first person.

5. The method of claim 3, further comprising calculating a mean of absolute values of vertical angular velocity, a mean of absolute values of vertical acceleration, or any combination thereof from the parameters of the measured subsequent motion data, and wherein the calculating the minimum ground clearance parameter of the first person is based on the calculated mean of absolute values of vertical angular velocity and based on the mean of absolute values of vertical acceleration.

6. The method of claim 3, wherein the calculating the minimum ground clearance parameter of the first person is based on a mean angular velocity at a mid-swing point, a minimum angular velocity, a maximum angular velocity, a coefficient of variation of angular velocity, or any combination thereof.

7. The method of claim 1, wherein the generated regression model is a quadratic regression model, an interaction regression model, or any combination thereof.

8. The method of claim 1, wherein the generated regression model is a linear regression model.

9. The method of claim 8, wherein a slope of a regression line derived from the linear regression model is based on the person'"'"'s weight, shank length, age, or any combination thereof.

10. The method of claim 1, further comprising generating a classifier model of falls risk based on the calculated minimum ground clearance parameter.

15. The method of claim 1, wherein the measured inertial sensor trial data comprises inertial sensor data from a left limb of the one or more persons and inertial sensor data from a right limb of the one or more persons, and

wherein the method further comprises:

generating, by the processor, separate regression models for the right limb of the one or more persons and for the left limb of the one or more persons, and

wherein the parameters of measured subsequent motion data for the first person input to the regression model comprises inertial sensor data from a left limb and a right limb of the first person.

17. The method of claim 4, further comprising one or more shank-mounted inertial sensors for measuring the measured inertial sensor trial data and the measured subsequent motion data of the first person.

18. The method of claim 1, further comprising one or more shank-mounted inertial sensors for measuring the measured inertial sensor trial data and the measured subsequent motion data of the first person.

11. A non-transitory computer-readable medium, the computer-readable medium comprising one or more instructions that, when executed by one or more processors, cause the one or more processors to perform a method for assessing falls risk in one or more persons, the processors being configured to:

collect data to generate a regression model, wherein the collecting of reference data comprises;

measuring inertial sensor trial data using one or more inertial sensors on one or more persons during a walking trial; and

measuring reference minimum ground clearance measurements associated with the one or more persons using an optical motion capture system having at least one camera during the walking trial;

generate a regression model using;

(a) the measured inertial sensor trial data from one or more inertial sensors associated with one or more persons and (b) the measured reference minimum ground clearance measurements from the optical capture system associated with the one or more persons collected as the reference data during the walking trial, wherein the regression model represents minimum ground clearance parameters as a function of one or more parameters of the motion data from the one or more inertial sensors; and

, after generating the regression model,

measuring subsequent motion data using one or more inertial sensors on a first person while walking;

calculate, using the generated regression model, a minimum ground clearance parameter of the first person, wherein an input to the regression model comprises parameters selected from the measured subsequent motion data of the first person, and wherein the calculation is performed without use of the optical motion capture system; and

generate a value indicative of a risk of fall for the first person based on the calculated minimum ground clearance parameter of the first person.

12. The non-transitory computer-readable medium of claim 11, wherein the parameters of the measured subsequent motion data comprises angular velocity, mean of the absolute values of angular velocity, maximum angular velocity, minimum angular velocity, coefficient of variation of angular velocity, or any combination thereof.

13. The non-transitory computer-readable medium of claim 12, wherein the selected parameters of the measured subsequent motion data comprises acceleration, mean of the absolute values of acceleration, maximum acceleration, minimum acceleration, coefficient of variation of acceleration, or any combination thereof.

14. The non-transitory computer-readable medium of claim 11, further comprising the processor configured to:

receive a second set of selected parameters of measured subsequent motion data using one or more inertial sensors; and

calculate a second set of minimum ground clearance data based on the second set of motion data and the generated regression model.

16. A system for assessing falls risk in one or more persons comprising:

one or more inertial sensors mounted on a body of a first person configured to measure motion data; and

a processor configured to;

collect data to generate a regression model, wherein the collecting of reference data comprises;

measuring inertial sensor trial data using one or more inertial sensors on one or more persons during a walking trial; and

measuring reference minimum ground clearance measurements associated with the one or more persons using an optical motion capture system having at least one camera during the walking trial;

generate a regression model using;

(a) the measured inertial sensor trial data from one or more inertial sensors associated with one or more persons and (b) the measured reference minimum ground clearance measurements from the optical capture system associated with the one or more persons collected as the reference data during the walking trial, wherein the regression model represents minimum ground clearance parameters as a function of one or more parameters of the motion data from the one or more inertial sensors;

thereafter,

measure subsequent motion data using one or more inertial sensors on a first person while walking;

calculate, using the generated regression model, a minimum ground clearance parameter of the first person, wherein an input to the regression model comprises parameters selected from the measured subsequent motion data of the first person, the motion data associated with the first person being measured using the one or more inertial sensors on the first person, and wherein the calculation is performed without use of the optical motion capture system; and

generate a value indicative of a risk of falling for the first person based on the calculated minimum ground clearance parameter associated with the first person.

19. The system of claim 16, wherein the measured inertial sensor trial data and the measured subsequent motion data of the first person comprises motion data measured from at least one foot-mounted inertial sensor and at least one shank-mounted inertial sensor.