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Lynne Grewe, Steven Magaa-Zook CSUEB, lynne.grewe@csueastbay.edu A cyber-physical system for senior collapse detection

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Seniors Falling Over 1/3rd of seniors above 65 fall each year Lead to serious injury and even death Falls account for 25% of all hospital admissions, and 40% of all nursing home admissions 40% of those admitted do not return to independent living; 25% die within a year. Fast medical attention can make a difference Many falls do not result in injuries, yet a large percentage of non-injured fallers (47%) cannot get up without assistance.

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Cost of Falling? 2005, CDC study Cost for Falls leading to fatality

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Goal create a smart home system to predict and detect the falling of senior/geriatric participants in home environments More seniors living at home autonomously

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SCD: Senior Collapse Detection Overview

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SCD: uses Kinect Sensor Inexpensive, commercial, well tested, good API support Modalityexample 2D 3D Audio

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Feature Extraction Perform Skeleton Tracking Ideal fall indicators often involve joint locations and range of motion Good Resolution 21 joints

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Skeleton Tracking Has Noise Degrading performance with occlusion General Twitching Also degrades as more occlusion from being on floor