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Wireless intelligent fall detection and movement classification using fuzzy logic
Author(s)
Date Issued
December 1, 2012
Type
Conference Paper
Abstract
Global population aging leads to increased interests in preventive healthcare technology. As falls are the most common cause of injury or death in old persons, fall detection and movement classification is one of the key topics in this research area. In this paper we propose a simple wireless intelligent system prototype for fall detection and movement classification for real-time monitoring of the elderly. The portable sensor unit acquires data from a triaxial accelerometer and sends the data wirelessly to a computer using Zigbee technology. Alternative to classic methods, the movement data is analyzed using a fuzzy inference system. The system is designed to distinguish between four movement types: standing, sitting, forward fall, and backward fall. Its classification accuracy is investigated using experimental data. It is observed that the system performs well with high sensitivity and excellent specificity. Additionally, the system is applicable for monitoring rehabilitative patients and is extendable to a larger class of movements and postures. ©2012 IEEE.
Citation
5th 2012 Biomedical Engineering International Conference Bmeicon 2012, 2012