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July, 2014
Friday

accidental falls

iFall: an Android application for fall monitoring and response

Sposaro, Frank, and Gary Tyson, Conf Proc IEEE Eng Med Biol Soc, 2009

Injuries due to falls are among the leading causes of hospitalization in elderly persons, often resulting in a rapid decline in quality of life or death. Rapid response can improve the patients outcome, but this is often lacking when the injured person lives alone and the nature of the injury complicates calling for help. This paper presents an alert system for fall detection using common commercially available electronic devices to both detect the fall and alert authorities. We use an Android-based smart phone with an integrated tri-axial accelerometer. Data from the accelerometer is evaluated with several threshold based algorithms and position data to determine a fall. The threshold is adaptive based on user provided parameters such as: height, weight, and level of activity.
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4 April 2010 | No Comments »
Categories: Journal Article | Keyword(s): , , , , , , ,

User-Based Motion Sensing and Fuzzy Logic for Automated Fall Detection in Older Adults

Boissy, Patrick et al, Telemedicine and e-Health. December 2007, 13(6)

Fall detection and early medical response are challenging and promising aspects of home healthcare for older adults. A two-step algorithm for falls analyzed accelerometer data for 750 test events and found significance limits for body trunk angle change as well as falls. Automated detection of falls based upon motion sensing and fuzzy logic can be based upon evidence-derived rules.
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8 January 2008 | No Comments »
Categories: Journal Article | Keyword(s): , , , , , ,

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