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.
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Lynne Grewe and Steven Magaña-Zook
A cyber-physical system for senior collapse detection
", Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 90911K (June 20, 2014); doi:10.1117/12.2049377; http://dx.doi.org/10.1117/12.2049377