Fall detection and fall risk detection systems and methods

• US 9,901,290 B2
• Filed: 03/16/2015
• Issued: 02/27/2018
• Est. Priority Date: 10/12/2007
• Status: Active Grant

• Current Assignee: BBY Solutions Incorporated (Best Buy Co., Inc.)
• Original Assignee: BioSensics, LLC (Best Buy Co., Inc.)
• Inventors: Najafi, Bijan, Vaziri, Ashkan, Boloori, Ali-Reza
• Primary Examiner(s): HENSON, DEVIN B
• Application Number:
  US14/659,446
• Publication Number:
  US 20160100776A1
• Time in Patent Office: 1,079 Days
• CPC Class Codes: A61B 2503/08 Elderly
• CPC Class Codes: A61B 2505/07 Home care
• CPC Class Codes: A61B 2505/09 Rehabilitation or training
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/1117 Fall detection
• CPC Class Codes: A61B 5/1118 Determining activity level
• 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 5/6823 Trunk, e.g., chest, back, abdomen, hip
• CPC Class Codes: A61B 5/6831 Straps, bands or harnesses
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• 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: 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: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: G08B 21/043 : detecting an emergency event, e.g. a fall
• 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
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

Abstract

The present invention relates to a light-weight, small and portable ambulatory sensor for measuring and monitoring a person'"'"'s physical activity. Based on these measurements and computations, the invented system quantifies the subject'"'"'s physical activity, quantifies the subject'"'"'s gait, determines his or her risk of falling, and automatically detects falls. The invention combines the features of portability, high autonomy, and real-time computational capacity. High autonomy is achieved by using only accelerometers, which have low power consumption rates as compared with gyroscope-based systems. Accelerometer measurements, however, contain significant amounts of noise, which must be removed before further analysis. The invention therefore uses novel time-frequency filters to denoise the measurements, and in conjunction with biomechanical models of human movement, perform the requisite computations, which may also be done in real time.

Claims

1. A body movement monitoring system comprising:

a data processing system comprising one or more processor circuits configured to process data generated by a sensor, the data including information representative of at least one signal generated by the sensor in response to movement of an upper part of a body of a person, the data processing system programmed to at least;

process said data to identify one or more peaks in the at least one signal by comparing one or more parameters with one or more predefined fall thresholds; and

for at least one identified peak of the one or more peaks;

process said data to identify a non-fall activity performed by the person during at least one of a time period before said identified peak or a time period after said identified peak; and

in response to identifying said non-fall activity;

determine whether said identified peak can be classified as a fall event or a non-fall event based on the one or more predefined fall thresholds;

in response to determining said identified peak can be classified as a fall event or a non-fall event based on the one or more predefined fall thresholds, classify said identified peak as a fall event or a non-fall event based on the one or more predefined fall thresholds; and

in response to determining said identified peak cannot be classified as a fall event or a non-fall event based on the one or more predefined fall thresholds;

 

set one or more new fall thresholds based on said non-fall activity;

 

process a portion of the data corresponding to a time window that encompasses said identified peak to compute one or more parameters; and

 

determine whether to classify said identified peak as a fall event by comparing said one or more parameters to said one or more new fall thresholds.

2. The system of claim 1 wherein said non-fall activity is walking, sitting, standing, or lying down.

3. The system of claim 1 wherein said one or more new fall thresholds are set based on at least one of the following:

body posture during the time period prior to the time window corresponding to the identified peak;

or

body posture during the time period following the time window corresponding to the identified peak.

4. The system of claim 1 wherein:

the sensor comprises at least one accelerometer; and

at least one of said one or more processor circuits is programmed to determine at least one posture of the person based on a direction of an acceleration measured by the at least one accelerometer, the acceleration being relative to a gravitational acceleration.

5. The system of claim 1 wherein said one or more parameters include or are calculated based on at least one of:

a speed at a time prior to said identified peak or during a time period that encompasses said identified peak;

an acceleration during a time period that encompasses said identified peak;

or

a mechanical energy during a time period that encompasses said identified peak.

8. The system of claim 1 further comprising a communications system configured to receive said data from said sensor.

9. The system of claim 1 further comprising the sensor.

10. The system of claim 9 wherein the sensor is attached to the upper part of the body.

11. The system of claim 9 wherein the sensor comprises at least one of the one or more processor circuits configured to process said data generated by said sensor.

12. The system of claim 11 wherein the at least one of the one or more processor circuits of the sensor is configured to at least partially process said data generated by said sensor.

13. The system of claim 9 wherein the sensor entirely houses the one or more processor circuits configured to process said data generated by said sensor.

14. The system of claim 1 wherein the data processing system comprising said one or more processor circuits configured to process said data generated by said sensor is housed separately from said sensor.

15. The system of claim 1 wherein said information derived from the person'"'"'s movement includes a history of fall events.

16. The system of claim 1 further comprising one or more data storage systems configured to store at least one of the data generated by said sensor or data processed by said one or more processor circuits.

17. The system of claim 1 wherein setting the one or more new fall threshold is based on a type of non-fall activity identified.

18. The system of claim 17 wherein said type of non-fall activity comprises walking, sitting, standing, or lying down.

19. The system of claim 18 wherein said type of non-fall activity comprises walking and said one or more new fall thresholds is based on either walking on a right step or walking on a left step.

20. The system of claim 1 wherein said one or more processor circuits are further configured to:

determine information related to physical activity of the person after a time period corresponding to the identified peak in response to classifying said identified peak as a fall event; and

classify the fall event as a serious fall event when the determined information indicates that the fall event is followed by a long period of rest or multiple unsuccessful postural transitions in succession.

21. The system of claim 1 wherein said one or more processor circuits are further configured to:

when the identified non-fall activity is not walking, segment a shock pattern identified based on the one or more peaks;

estimate a width of the shock pattern; and

estimate a vertical speed of the person in a time period prior to a time period corresponding to the shock pattern.

6. The system of 5 wherein said one or more processor circuits are further programmed to set said one or more new fall thresholds based at least in part on information derived from the person'"'"'s movement over a period of time prior to said identified peak.

7. The system of 6 wherein said information derived from the person'"'"'s movement includes at least one of:

a measure of walking;

or

a measure of postural transitions.

22. A body movement monitoring system comprising:

a data processing system comprising one or more processor circuits configured to process data generated by a sensor, the data including information representative of at least one signal generated by the sensor in response to movement of an upper part of a body of a person, the data processing system programmed to at least;

process said data to identify one or more peaks in the at least one signal by comparing one or more parameters with one or more predefined fall thresholds; and

for at least one identified peak of the one or more peaks;

process said data to identify a non-fall activity performed by the person during at least one of a time period before said identified peak or a time period after said identified peak; and

in response to identifying said non-fall activity;

set one or more new fall thresholds based on said non-fall activity;

process a portion of the data corresponding to a time window that encompasses said identified peak to compute one or more parameters; and

determine whether to classify said identified peak as a fall event by comparing said one or more parameters to said one or more new fall thresholds.

23. The system of claim 22 further comprising the sensor, wherein the sensor is attached to the upper part of the body.

24. The system of claim 22 wherein said non-fall activity is walking, sitting, standing, or lying down.

25. The system of claim 22 wherein said one or more new fall thresholds are set based on at least one of the following:

body posture during the time period prior to the time window corresponding to the identified peak;

or

body posture during the time period following the time window corresponding to the identified peak.

26. The system of claim 22 further comprising the sensor, wherein:

the sensor comprises at least one accelerometer; and

at least one of said one or more processor circuits is programmed to determine at least one posture of the person based on a direction of an acceleration measured by the at least one accelerometer, the acceleration being relative to a gravitational acceleration.

27. The system of claim 22 further comprising a communications system configured to receive said data from said sensor.

28. The system of claim 22 wherein the data processing system comprising said one or more processor circuits configured to process said data generated by said sensor is housed separately from said sensor.

29. The system of claim 22 wherein said information derived from the person'"'"'s movement includes a history of fall events.

30. The system of claim 22 wherein said one or more processor circuits are further configured to:

determine information related to physical activity of the person after a time period corresponding to the identified peak in response to classifying said identified peak as a fall event; and

classify the fall event as a serious fall event when the determined information indicates that the fall event is followed by a long period of rest or multiple unsuccessful postural transitions in succession.

31. The system of claim 22 wherein said one or more processor circuits are further configured to:

when the identified non-fall activity is not walking, segment a shock pattern identified based on the one or more peaks;

estimate a width of the shock pattern; and

estimate a vertical speed of the person in a time period prior to a time period corresponding to the shock pattern.