A WEARABLE REAL-TIME FALL DETECTOR BASED ON NAIVE BAYES CLASSIFIER

被引：0
|
作者：
Yang, Xiuxin [1 ]
Dinh, Anh [1 ]
Chen, Li [1 ]
机构：
[1] Univ Saskatchewan, Dept Elect & Comp Engn, Saskatoon, SK, Canada
来源：
2010 23RD CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE) | 2010年
关键词：
fall detection; Naive Bayes classifier; Sun SPOT; accelerometer; machine learning;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, we implement a wearable real-time system using the Sun SPOT wireless sensors embedded with Naive Bayes algorithm to detect fall. Naive Bayes algorithm is demonstrated to be better than other algorithms both in accuracy performance and model building time in this particular application. At 20Hz sampling rate, two Sun SPOT sensors attached to the chest and the thigh provide acceleration information to detect forward, backward, leftward and rightward falls with 100% accuracy as well as overall 87.5% sensitivity.
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页数：4
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