Applying online feature selection for fall detection
Donghui Wu, Zhelong Wang · 2016
Recently, sensor based human activity recognition has attracted lots of researches, which overcomes the constraint of the traditional vision-based technology. Fall detection is an important part of monitoring elderly people. In order to fast and precisely detect the action fall from huge data, online feature selection method is proposed to improve online human activity recognition, which overcomes the limit of traditional batch learning methods. The high-dimensional real-world dataset is utilized to evaluate the proposed method. Better results have demonstrated the efficacy of the proposed online feature selection approach. It has also been demonstrated that data from belt is much more easily discriminated than from other three body parts.