Method and system for fall detection of a user

• US 9,588,135 B1
• Filed: 03/14/2012
• Issued: 03/07/2017
• Est. Priority Date: 11/14/2011
• Status: Active Grant

• Current Assignee: Vital Connect, Inc.
• Original Assignee: Vital Connect, Inc.
• Inventors: Narasimhan, Ravi
• Primary Examiner(s): Wilson, Brian
• Application Number:
  US13/420,382
• Time in Patent Office: 1,819 Days
• Field of Search: 340/669, 340/539.12, 340/573.1, 340/573.7
• US Class Current: 1/1
• CPC Class Codes: G01P 15/00 Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
• CPC Class Codes: G08B 21/02 Alarms for ensuring the safety of persons
• 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

Abstract

A method and system for fall detection of a user are disclosed. In a first aspect, the method comprises determining whether first or second magnitude thresholds are satisfied. If the first or second magnitude thresholds are satisfied, the method includes determining whether an acceleration vector of the user is at a predetermined angle to a calibration vector or whether the user is at a stooped posture. If the acceleration vector of the user is at the predetermined angle to the calibration vector or if the user is at the stooped posture, the method includes determining whether an activity metric is satisfied.

Claims

1. A method for fall detection of a user, the method comprising:

determining whether first or second magnitude thresholds are satisfied;

if the first or second magnitude thresholds are satisfied, determining whether an acceleration vector of the user is at a predetermined angle to a calibration vector or whether the user is at a stooped posture; and

if the acceleration vector of the user is at the predetermined angle to the calibration vector or if the user is at the stooped posture, determining whether an activity metric is satisfied, wherein if a moving average of the user is greater than a predetermined activity level, the activity metric is not satisfied and notification information of the fall detection is disregarded, wherein if the moving average of the user is lesser than the predetermined activity level, the activity metric is satisfied and the notification information of the fall detection is confirmed,

wherein determining whether first or second magnitude thresholds are satisfied further comprises;

obtaining an acceleration sample from the user;

comparing the acceleration sample to a first acceleration threshold;

wherein if the acceleration sample is less than the first acceleration threshold, the first magnitude threshold is satisfied, else comparing the acceleration sample to a second acceleration threshold;

wherein if the acceleration sample is greater than the second acceleration threshold, the second magnitude threshold is satisfied, and

wherein comparing the acceleration sample to the first acceleration threshold further comprises;

applying two filters to the acceleration sample to output an acceleration vector;

calculating L^P-norm of the acceleration vector to output an acceleration scalar; and

comparing the acceleration scalar to the first acceleration threshold.

2. The method of claim 1, wherein comparing the acceleration sample to the second acceleration threshold further comprises:

comparing the acceleration scalar to the second acceleration threshold.

3. The method of claim 1, wherein L^P-norm is any of L¹-norm, L²-norm, L^∞

-norm and the two filters are any of single-pole infinite impulse response (IIR) filters, multiple-pole IIR filters, finite impulse response (FIR) filters and median filters.

4. The method of claim 1, wherein the two filters are any of single-pole infinite impulse response (IIR) filters, multiple-pole IIR filters, finite impulse response (FIR) filters and median filters.

5. The method of claim 1, wherein determining whether an acceleration vector of the user is at a predetermined angle to a calibration vector further comprises:

attaching a wireless sensor device to the user;

determining the calibration vector, wherein the calibration vector is an acceleration vector when the user is vertical;

obtaining at least one acceleration sample from the wireless sensor device;

comparing the at least one acceleration sample to the calibration vector; and

wherein if the at least one acceleration sample is nearly orthogonal to the calibration vector, detecting the fall of the user.

6. The method of claim 5, wherein determining the calibration vector further comprises:

attaching the wireless sensor device when the user is vertical; and

measuring an acceleration sample after attachment, wherein the acceleration sample is determined to be the calibration vector.

7. The method of claim 5, wherein determining the calibration vector further comprises:

measuring an acceleration sample after the user is walking, wherein the acceleration sample is determined to be the calibration vector.

8. The method of claim 5, wherein the wireless sensor device is attached, in any orientation, to the user.

9. The method of claim 1, further comprising:

wherein if the first or second magnitude thresholds are satisfied, waiting a predetermined time period before determining whether the acceleration vector of the user is at the predetermined angle to the calibration vector or whether the user is at the stooped posture.

10. The method of claim 1, further comprising:

relaying the notification information of the fall detection of the user to another user or device.

11. The method of claim 1, wherein determining whether an activity metric is satisfied further comprises:

applying an IIR band-pass filter to the acceleration vector of the user to produce an activity level vector of an activity level of the user;

calculating L¹-norm of the activity level vector to output an activity level scalar;

calculating the moving average of the activity level scalar over a time period; and

comparing the moving average to the predetermined activity level.

12. The method of claim 1, wherein determining whether the user is at a stooped posture further comprises:

calculating a z-axis component of the calibration vector; and

comparing the z-axis component of the calibration vector to a predetermined level.

13. A wireless sensor device for fall detection of a user, the wireless sensor device comprising:

a processing system; and

an application to be executed by the processing system, wherein the application;

determines whether first or second magnitude thresholds are satisfied,

if the first or second magnitude thresholds are satisfied, determines whether an acceleration vector of the user is at a predetermined angle to a calibration vector or whether the user is at a stooped posture, and

if the acceleration vector of the user is at the predetermined angle to the calibration vector or if the user is at the stooped posture, determines whether an activity metric is satisfied, wherein if a moving average of an activity level of the user is greater than a predetermined activity level, the activity metric is not satisfied and notification information of the fall detection is disregarded, wherein if the moving average of the activity level of the user is lesser than the predetermined activity level, the activity metric is satisfied and the notification information of the fall detection is confirmed,

wherein the application further;

obtains an acceleration sample from the user;

compares the acceleration sample to a first acceleration threshold;

wherein if the acceleration sample is less than the first acceleration threshold, the first magnitude threshold is satisfied, else the application compares the acceleration sample to a second acceleration threshold;

wherein if the acceleration sample is greater than the second acceleration threshold, the second magnitude threshold is satisfied, wherein the application further;

applies two filters to the acceleration sample to output an acceleration vector;

calculates L^P-norm of the acceleration vector to output an acceleration scalar; and

compares the acceleration scalar to the first acceleration threshold or to the second acceleration threshold.

14. The wireless sensor device of claim 13, wherein the application determines whether an activity metric is satisfied further comprises that the application:

applies an IIR band-pass filter to the acceleration vector of the user to produce an activity level vector of the user;

calculates L¹-norm of the activity level vector to output an activity level scalar;

calculates the moving average of the activity level scalar over a time period; and

compares the moving average to the predetermined activity level.

15. The wireless sensor device of claim 13, wherein if the first or second magnitude thresholds are satisfied, if the acceleration vector of the user is at the predetermined angle to the calibration vector or the user is at the stooped posture, and if the activity metric is satisfied, the application further:

relays the notification information of the fall detection of the user to another user or device.