A wearable real-time fall detector based on Naive Bayes classifier
Xiuxin Yang, Anh Dinh, Li Chen · 2010
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.