Method for quantifying the risk of falling of an elderly adult using an instrumented version of the FTSS test

• US 9,877,667 B2
• Filed: 09/12/2012
• Issued: 01/30/2018
• Est. Priority Date: 09/12/2012
• Status: Active Grant

• Current Assignee: Linus Health Europe Limited
• Original Assignee: Care Innovations LLC
• Inventors: Doheny, Emer P.
• Primary Examiner(s): Breene, John
• Assistant Examiner(s): LIAO, CHRISTINE Y
• Application Number:
  US13/611,012
• Publication Number:
  US 20140074442A1
• Time in Patent Office: 1,966 Days
• Field of Search: 703 2
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• 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 5/7275 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: G08B 21/0446 worn on the body to detect changes of posture, e.g. a fall, inclination, acceleration, gait
• CPC Class Codes: G08B 21/0453 worn on the body to detect health condition by physiological monitoring, e.g. electrocardiogram, temperature, breathing detecting, measuring or recording for diagnostic purposes A61B5/00
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Methods and systems may provide for estimating falls risk based on inertial sensor data collected during a Five Times Sit-to-Stand (FTSS) test. In an embodiment, a classifier model may be trained with inertial sensor data collected from a sample of people performing the FTSS test and their self-reported falls history. In an embodiment, one or more features related to steadiness or smoothness of the person'"'"'s movement may be calculated. In an embodiment, one or more features related to timing of the FTSS test, such as a total time taken to complete the FTSS test or to complete individual sit-stand-sit (SSS) phases of the test, may be calculated. In an embodiment, supervised pattern recognition techniques may train the classifier model to classify a person as being likely to fall or not being likely to fall based on FTSS-related feature values collected from that person.

Claims

1. A computer-implemented method for estimating falls risk, comprising:

measuring first acceleration data from a first inertial sensor and second acceleration data from a second inertial sensor, the first and second inertial sensors comprise tri-axial accelerometers and are attached to at least one person transitioning at least one times from a standing state to a sitting state or from a sitting state to a standing state, wherein the first inertial sensor is attached to the person'"'"'s lower body and the second inertial sensor is attached to the person'"'"'s upper body;

receiving, at one or more processors, the first acceleration data from the first inertial sensor attached to the at least one person;

receiving, at the one or more processors, the second acceleration data from the second inertial sensor attached to the at least one person;

receiving, at the one or more processors, falls history information of the at least one person;

determining, from the first acceleration data and using the one or more processors, a first value of one or more features indicating a total time for the at least one person to complete the transitioning between the sitting state and the standing state;

determining, from the second acceleration data and using the one or more processors, a second value of the one or more features indicating steadiness of movement of the at least one person, wherein a jerk of the at least one person'"'"'s movement is calculated as a derivative of second acceleration data along a sensor axis to measure the steadiness of movement of the least one person along the sensor axis;

determining, from the second acceleration data and using the one or more processors, a third value of the one or more features indicating a mean, coefficient of variation, or root mean square of the second acceleration data and a fourth value of the one or more features indicating a spectral edge frequency of the second acceleration data;

generating, at the one or more processors, a classifier model based on the determined first value, the determined second value, the determined third value, the determined fourth value, and the falls history information of the at least one person;

after the generation of the classifier model, the one or more processors receiving acceleration data from inertial sensors attached to another person;

inputting a subset of the one or more features as input features of the classifier model;

calculating, via the one or more processors using both the generated classifier model and the received acceleration data from the another person, a quantitative value for a probability of a risk of falling of the another person; and

outputting a classification of likely to fall or not being likely to fall for the another person based on the calculated probability using the one or more processors.

2. The computer-implemented method of claim 1, wherein the classifier model is a linear discriminant classifier model.

3. The computer-implemented method of claim 1, wherein the one or more processors further utilize the at least one person'"'"'s age to generate the classifier model.

4. The computer-implemented method of claim 1, wherein the total time is calculated as a difference between a time corresponding to a start of a first transition and a time corresponding to an end of a fifth transition.

5. The computer-implemented method of claim 1, wherein the method further comprises calculating additional values based on the first and/or second acceleration data, the additional values indicating an acceleration amplitude along a person'"'"'s body axis and a mean jerk of the person'"'"'s movement along the person'"'"'s body axis, and wherein the method further comprises using the additional values relating to the acceleration amplitude and the mean jerk to generate the classifier model.

6. The method of claim 1, wherein the first and second inertial sensors are attached to thigh and torso of the at least one person.

7. The method of claim 1, wherein the at least one times from a standing state to a sitting state or from a sitting state to a standing state comprises at least five times from a standing state to a sitting state or from a sitting state to a standing state.

8. A non-transitory computer-readable medium having instructions that, when executed by one or more processors, cause the one or more processors to:

receive first acceleration data from a first inertial sensor attached to at least one person transitioning at least one times from a standing state to a sitting state or from a sitting state to a standing state;

receive second acceleration data from a second inertial sensor attached to the at least one person taken during the transitioning at least one times, wherein the first and second inertial sensors comprise tri-axial accelerometers;

receive falls history information of the at least one person;

determine, from the first acceleration data, a first value of one or more features indicating a total time for the at least one person to complete the transitioning between the sitting state and the standing state;

determine, from the second acceleration data, a second value of one or more features indicating steadiness of movement of the at least one person;

determining, from the second acceleration data, a third value of one or more features indicating a mean, coefficient of variation, or root mean square of the second acceleration data and a fourth value of one or more features indicating a spectral edge frequency of the second acceleration data, wherein a jerk of the at least one person'"'"'s movement is calculated as a derivative of second acceleration data along a sensor axis to measure the steadiness of movement of the least one person along the sensor axis;

generate a classifier model based on the determined first value, the determined second value, the determined third value, the determined fourth value, and the falls history information of the at least one person,

wherein the first inertial sensor is attached to the person'"'"'s lower body and the second sensor is attached to the person'"'"'s upper body, and,

after the generating of the classifier model, receiving acceleration data from inertial sensors attached to another person;

inputting a subset of the one or more features as input features of the classifier model;

calculating, using both the generated classifier model and the received acceleration data from the another person, a quantitative value for a probability of a risk of falling of the another person; and

outputting a classification of likely to fall or not being likely to fall for the another person based on the calculated probability.

9. The non-transitory computer-readable medium of claim 8, wherein the instructions cause the one or more processors to generate a linear discriminant classifier model.

10. The non-transitory computer-readable medium of claim 8, wherein the one or more processors further utilize the at least one person'"'"'s age to generate the classifier model.

11. The non-transitory computer-readable medium of claim 8, wherein the total time is calculated as a difference between a time corresponding to a start of a first transition and a time corresponding to an end of a fifth transition.

12. The non-transitory computer-readable medium of claim 8, wherein the method further comprises calculating additional values based on the first and/or second acceleration data, the additional values indicating an acceleration amplitude along a person'"'"'s body axis and a mean jerk of the person'"'"'s movement along the person'"'"'s body axis, and wherein the method further comprises using the additional values relating to the acceleration amplitude and the mean jerk to generate the classifier model.

13. The method of claim 8, wherein the at least one times from a standing state to a sitting state or from a sitting state to a standing state comprises at least five times from a standing state to a sitting state or from a sitting state to a standing state.