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Proceedings Paper

A cyber-physical system for senior collapse detection
Author(s): Lynne Grewe; Steven Magaña-Zook
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Paper Abstract

Senior Collapse Detection (SCD) is a system that uses cyber-physical techniques to create a “smart home” system to predict and detect the falling of senior/geriatric participants in home environments. This software application addresses the needs of millions of senior citizens who live at home by themselves and can find themselves in situations where they have fallen and need assistance. We discuss how SCD uses imagery, depth and audio to fuse and interact in a system that does not require the senior to wear any devices allowing them to be more autonomous. The Microsoft Kinect Sensor is used to collect imagery, depth and audio. We will begin by discussing the physical attributes of the “collapse detection problem”. Next, we will discuss the task of feature extraction resulting in skeleton and joint tracking. Improvements in error detection of joint tracking will be highlighted. Next, we discuss the main module of “fall detection” using our mid-level skeleton features. Attributes including acceleration, position and room environment factor into the SCD fall detection decision. Finally, how a detected fall and the resultant emergency response are handled will be presented. Results in a home environment will be given.

Paper Details

Date Published: 20 June 2014
PDF: 11 pages
Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 90911K (20 June 2014); doi: 10.1117/12.2049377
Show Author Affiliations
Lynne Grewe, California State Univ., East Bay (United States)
Steven Magaña-Zook, California State Univ., East Bay (United States)

Published in SPIE Proceedings Vol. 9091:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII
Ivan Kadar, Editor(s)

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