Title :
A wearable real-time fall detector based on Naive Bayes classifier
Author :
Yang, Xiuxin ; Dinh, Anh ; Che, Li
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Saskatchewan, Saskatoon, SK, Canada
Abstract :
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.
Keywords :
Bayes methods; accelerometers; biomechanics; geriatrics; medical signal processing; signal classification; wireless sensor networks; Naive Bayes classifier; Sun SPOT wireless sensors; acceleration; accuracy performance; model building time; wearable real-time fall detector; Acceleration; Accuracy; Classification algorithms; Sensor phenomena and characterization; Testing; Training; Naive Bayes classifier; Sun SPOT; accelerometer; fall detection; machine learning;
Conference_Titel :
Electrical and Computer Engineering (CCECE), 2010 23rd Canadian Conference on
Conference_Location :
Calgary, AB
Print_ISBN :
978-1-4244-5376-4
Electronic_ISBN :
0840-7789
DOI :
10.1109/CCECE.2010.5575129
