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论文类型：会议论文

发表时间：2016-10-04

收录刊物：EI、CPCI-S、SCIE、Scopus

页面范围：418-422

关键字：Feature selection; Online learning; Activity recognition; Big data analytics

摘要：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.