ACTIVITY ANALYSIS, FALL DETECTION AND RISK ASSESSMENT SYSTEMS AND METHODS

• US 20170143240A1
• Filed: 02/03/2017
• Published: 05/25/2017
• Est. Priority Date: 04/27/2012
• Status: Active Grant

• Current Assignee: Curators of the University of Missouri (University of Missouri)
• Original Assignee: Curators of the University of Missouri (University of Missouri)
• Inventors: Stone, Erik Edward, Skubic, Marjorie, Rantz, Marilyn, Popescu, Mihail
• Granted Patent: US 10,080,513 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/0013 Medical image data A61B1/00011, A61B6/56, A61B8/56 take precedence
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/004 adapted for image acquisition of a particular organ or body part A61B5/0082 takes precedence; arrangements for optical scanning A61B5/0062
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/1113 Local tracking of patients, e.g. in a hospital or private home
• CPC Class Codes: A61B 5/1117 Fall detection
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/1128 using image analysis A61B5/1127 takes precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes 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: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/30196 Human being; Person
• CPC Class Codes: G06T 2207/30241 Trajectory
• CPC Class Codes: G06T 7/194 involving foreground-background segmentation
• CPC Class Codes: G06T 7/215 Motion-based segmentation
• CPC Class Codes: G06T 7/285 using a sequence of stereo image pairs
• CPC Class Codes: G06V 40/23 Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: G16H 40/67 for remote operation
• 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

A method for determining the risk of a person falling is provided. The method includes acquiring depth image data that comprises a plurality of frames that depict a person walking through a home, and extracting a foreground object from the depth image data. The method additionally includes generating a three-dimensional data object based on the foreground object, and identifying a walking sequence from the three-dimensional data object. The method further includes generating one or more gait parameters from the identified walking sequence, and comparing the one or more gait parameters against a standard clinical measure of the one or more gait parameters to determine a level of risk at which the person is of falling.

Claims

1. A method for determining the risk of a person to falling, the method comprising:

acquiring by at least one processor of a computer-based remote device, depth image data from at least one depth camera, wherein the depth image data comprises a plurality of frames that depict the person walking through a home environment over time, the frames comprising a plurality of pixels, the remote device located remotely from the at least one depth camera, the remote device comprising electronic memory on which an image analysis application is electronically stored, and the at least one processor structured and operable to execute the image analysis application;

extracting, by the at least one processor, a foreground object from the depth image data;

segmenting, by the at least one processor, the pixels of the frames of the depth image data corresponding to the foreground object;

generating, by the at least one processor, a three-dimensional data object based on the foreground object;

tracking by the at least one processor, the three-dimensional data object over a plurality of frames of the depth images data;

identifying, by the at least one processor, a walking sequence from the tracked three-dimensional data object, wherein the identifying comprises;

the at least one processor determining a speed for the tracked three-dimensional data object over a time frame;

the at least one processor comparing the determined speed with a speed threshold;

in response to the comparison indicating that the determined speed is greater than the speed threshold, the at least one processor assigning a state indicative of walking to the tracked three-dimensional data object;

while the tracked three-dimensional data object is in the assigned walking state;

the at least one processor determining a walk straightness for the tracked three-dimensional data object;

the at least one processor determining a walk length for the tracked three-dimensional data object;

the at least one processor determining a walk duration for the tracked three-dimensional data object;

the at least one processor saving the tracked three-dimensional data object in memory as the identified walking sequence if the determined walk straightness exceeds a straightness threshold, the determined walk length exceeds a walk length threshold, and the determined walk duration exceeds a walk duration threshold;

generating, by the at least one processor, one or more gait parameters from the identified walking sequence;

comparing, by the at least one processor, the one or more gait parameters against a standard clinical measure of the one or more gait parameters to determine a level of risk at which the person is of falling.

2. The method of claim 1, wherein the identified walking sequence is compared against a previously saved walking sequence of the person to confirm that the identified walking sequence is correctly associated with the person.

3. The method of claim 2, wherein the comparison utilizes a Gaussian distribution.

4. The method of claim 1, wherein the one or more gait parameters includes at least one of:

walking speed, stride time, or stride length.

5. The method of claim 1, wherein the standard clinical measure is selected from the group consisting of:

Timed-Up-and-Go (TUG) and Habitual Gait Speed (HGS).