Ambulatory system for measuring and monitoring physical activity and risk of falling and for automatic fall detection

• US 9,005,141 B1
• Filed: 06/22/2012
• Issued: 04/14/2015
• 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:
  US13/531,313
• Time in Patent Office: 1,026 Days
• Field of Search: 600/587, 600/595
• US Class Current: 600/595
• 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, said data processing system configured to process acceleration measurements generated by a sensor, the sensor adapted to be attached to an upper part of a body of a person and comprising at least one accelerometer component adapted to generate one or more acceleration signals in response to movement of the body, the data processing system programmed to;

apply a filter to at least a portion of at least one of the one or more acceleration signals to obtain one or more filtered signals;

determine an estimated postural transition duration for a postural transition based at least partly on one or more peaks in the one or more filtered signals;

derive information related to the postural transition using the estimated postural transition duration, the information including at least one of;

time of occurrence of the postural transition, duration of the postural transition, or type of the postural transition;

apply a filter to a vertical accelerometer signal of the one or more acceleration signals for a time period corresponding to said duration of said postural transition, to obtain a filtered vertical acceleration signal;

compute a gradient of the filtered vertical acceleration signal to obtain a gradient signal;

identify a global extremum of said gradient signal;

if the magnitude of the global extremum exceeds a threshold value, compare a first portion of the filtered vertical acceleration signal that precedes the global extremum to a second portion of the filtered vertical acceleration signal that is subsequent to the global extremum;

if said comparison yields a first result, identify a lying posture; and

if said comparison yields a second result, identify a transition from said lying posture.

2. The body movement monitoring system of claim 1, wherein said data processing system is further configured to determine a revised estimated postural transition duration that is more accurate than the estimated postural transition duration.

3. The body movement monitoring system of claim 2, wherein said data processing system is further programmed to derive at least part of the information by:

estimating an inertial acceleration of the person during a time period corresponding to the revised estimated postural transition duration,

said estimating comprising processing a frontal acceleration signal of the one or more acceleration signals to correct for a sagittal trunk tilt angle of the person during flail the time period corresponding to the revised estimated postural transition duration.

4. The body movement monitoring system of claim 2, wherein said data processing system is further programmed to derive at least part of the information by:

estimating an inertial acceleration of the person during a time period corresponding to the revised estimated postural transition duration, said estimating comprising;

applying one or more filters to a vertical acceleration signal of the one or more acceleration signals from the time period corresponding to the revised estimated postural transition duration to obtain one or more filtered versions of the vertical acceleration signal; and

identifying one or more peaks in the one or more filtered versions of the vertical acceleration signal.

5. The body movement monitoring system of claim 2, wherein said data processing system is further programmed to derive at least part of the information by at least:

estimating an acceleration of the person during a time period corresponding to the revised estimated postural transition duration, said estimating comprising;

processing a frontal acceleration signal of the one or more acceleration signals to correct for a sagittal trunk tilt angle of the person during the time period corresponding to the revised estimated postural transition duration to obtain a corrected frontal acceleration signal;

using the corrected frontal acceleration signal and a vertical acceleration signal of the one or more acceleration signals, estimating time-varying inertial frontal and vertical acceleration signals;

removing a gravitational component from the estimated time-varying inertial vertical acceleration signal;

computing a time-varying magnitude signal corresponding to a square root of a sum of squares of said estimated time-varying inertial frontal and vertical acceleration signals;

applying at least one filter to said time-varying magnitude signal to obtain a filtered time-varying magnitude signal;

identifying a local extremum in said filtered time-varying magnitude signal preceding the time of occurrence of said postural transition (P_pre); and

identifying a local extremum in said filtered time-varying magnitude signal following the time of occurrence of said postural transition (P_post).

6. The body movement monitoring system of claim 5, wherein said data processing system is further programmed to perform at least one of:

classifying said postural transition as a transition to standing if P_pre is a local maximum and P_post is a local minimum;

or

classifying said postural transition as a transition to sitting if P_pre is a local minimum and P_post is a local maximum.

7. The body movement monitoring system of claim 6, wherein said data processing system is further configured to reclassify said postural transition based on a classification of one or more postural transitions preceding or following said postural transition.

8. The body movement monitoring system of claim 1, wherein the postural transition is classified based on analysis of a positive extremum and a negative extremum of one or more identified peaks in one or more filtered versions of a vertical acceleration signal of the one or more acceleration signals.

9. The body movement monitoring system of claim 8, wherein the postural transition is classified as:

a postural transition to standing when the positive extremum is followed by the negative extremum, and

a postural transition to sitting when the negative extremum is followed by the positive extremum.

10. The body monitoring system of claim 9, wherein said data processing system is further configured to reclassify said postural transition based on the classification of one or more postural transitions preceding or following said postural transition.

11. The body movement monitoring system of claim 1, wherein said data processing system is further configured to:

identify a first local extremum which precedes said global extremum, wherein said first portion of the filtered vertical acceleration signal corresponds to a time period preceding the first local extremum;

identify a second local extremum which follows said global extremum, wherein said second portion of the filtered vertical acceleration signal corresponds to a time period following the second local extremum, wherein

the comparison yields the first result when (1) an average of said first portion of the filtered vertical signal exceeds a second threshold and (2) an average of said second portion of the filtered vertical signal is lower than a third threshold; and

the comparison yields the second result when (1) an average of said first portion of the filtered vertical signal is less than a fourth threshold and (2) an average of said second portion of the filtered vertical signal exceeds a fifth threshold.

12. The body movement monitoring system of claim 11, wherein said data processing system is further programmed to, if said comparison yields the first result, classify the lying posture as lying on back, lying on front, or lying on side, using a frontal accelerometer signal of said one or more acceleration signals.

13. The body movement monitoring system of claim 12, wherein said data processing system is programmed to distinguish a side on which the user lies, using a lateral accelerometer signal of said one or more acceleration signals.

14. The body movement monitoring system of claim 1, further including a wireless transmission system for wirelessly transmitting at least a portion of said one or more acceleration signals, at least a portion of the information derived by said data processing system, or both.

15. The body movement monitoring system of claim 1, wherein said at least one accelerometer component comprises a tri-axial accelerometer.

16. The body movement monitoring system of claim 1, wherein said at least one accelerometer component comprises a plurality of mono-axial accelerometers configured to measure accelerations in a plurality of perpendicular directions.

17. The body movement monitoring system of claim 1, further comprising one or more data storage systems configured to store said one or more acceleration signals, said derived information, or both.

18. The body movement monitoring system of claim 1, wherein said data processing system and said sensor reside in a sensing module that is supported by a patient during use.

19. The body movement monitoring system of claim 1, wherein said data processing system resides in a computer that is separate from the sensor.

20. The body movement monitoring system of claim 1, wherein said data processing system does not utilize gyroscope data in deriving the information related to the postural transition.

21. The body movement monitoring system of claim 1, wherein the information comprises time of occurrence of the postural transition.

22. The body movement monitoring system of claim 1, wherein the information comprises duration of the postural transition.

23. The body movement monitoring system of claim 1, wherein the information comprises type of the postural transition.

24. A method for monitoring body movement, the method comprising:

receiving acceleration measurements corresponding to one or more acceleration signals generated by at least one accelerometer component of a sensor unit attached to an upper part of a body of a person in response to movement of the body;

with a data processing system comprising one or more processor circuits;

applying a filter to at least a portion of at least one of the one or more acceleration signals to obtain one or more filtered signals;

determining an estimated postural transition duration for a postural transition based at least partly on one or more peaks in the one or more filtered signals;

deriving information related to the postural transition using the estimated postural transition duration, the information including at least one of;

time of occurrence of the postural transition, duration of the postural transition, or type of the postural transition;

determining a revised estimated postural transition duration that is more accurate than the estimated postural transition duration; and

deriving at least part of the information by estimating an acceleration of the person during a time period corresponding to the revised estimated postural transition duration, said estimating comprising;

processing a frontal acceleration signal of the one or more acceleration signals to correct for a sagittal trunk tilt angle of the person during the time period corresponding to the revised estimated postural transition duration to obtain a corrected frontal acceleration signal;

using the corrected frontal acceleration signal and a vertical acceleration signal of the one or more acceleration signals, estimating time-varying inertial frontal and vertical acceleration signals;

removing a gravitational component from the estimated time-varying inertial vertical acceleration signal;

computing a time-varying magnitude signal corresponding to a square root of a sum of squares of said estimated time-varying inertial frontal and vertical acceleration signals;

applying at least one filter to said time-varying magnitude signal to obtain a filtered time-varying magnitude signal;

identifying a local extremum in said filtered time-varying magnitude signal preceding the time of occurrence of said postural transition (P_pre); and

identifying a local extremum in said filtered time-varying magnitude signal following the time of occurrence of said postural transition (P_post).

25. The method of claim 24, further comprising deriving, with the data processing system, at least part of the information by:

estimating an inertial acceleration of the person during a time period corresponding to the revised estimated postural transition duration,

said estimating comprising processing a frontal acceleration signal of the one or more acceleration signals to correct for a sagittal trunk tilt angle of the person during a time period corresponding to the revised estimated postural transition duration.

26. The method of claim 24, further comprising deriving, with the data processing system, at least part of the information by:

estimating an inertial acceleration of the person during a time period corresponding to the revised estimated postural transition duration, said estimating comprising;

applying one or more filters to a vertical acceleration signal of the one or more acceleration signals from the time period corresponding to the revised estimated postural transition duration to obtain one or more filtered versions of the vertical acceleration signal; and

identifying one or more peaks in the one or more filtered versions of the vertical acceleration signal.

27. Non-transitory computer storage that stores executable code that directs a computer system to at least:

apply a filter to at least a portion of at least one of one or more acceleration signals generated by at least one accelerometer component of a sensor unit attached to an upper part of a body of a person, the one or more acceleration signals generated in response to movement of the body;

determine an estimated postural transition duration for a postural transition based at least partly on one or more peaks in the at least a portion of the one or more acceleration signals that is filtered;

using the estimated postural transition duration, derive information related to the postural transition, the information including at least one of;

time of occurrence of the postural transition, duration of the postural transition, or type of the postural transition;

apply a filter to a vertical accelerometer signal of the one or more acceleration signals for a time period corresponding to said duration of said postural transition, to obtain a filtered vertical acceleration signal;

compute a gradient of the filtered vertical acceleration signal to obtain a gradient signal;

identify a global extremum of said gradient signal;

if the magnitude of the global extremum exceeds a threshold value, compare a first portion of the filtered vertical acceleration signal that precedes the global extremum to a second portion of the filtered vertical acceleration signal that is subsequent to the global extremum;

if said comparison yields a first result, identify a lying posture; and

if said comparison yields a second result, identify a transition from said lying posture.