关键词: 人类行为识别, 随机森林, 稀疏局部保持投影, 智能手机

Abstract: Recognition of human activity from the smartphone of sensory data has many important applications in many fields, such as healthcare services, intelligent environments and cyber security. Classification accuracy of most existed methods is not enough in many applications, especially for healthcare services. In order to improve accuracy, the paper proposes a Random Forest（RF） approach to recognize human activities and choose Sparse Local Preserving Projection（SpLPP） as the method of feature reduction. Firstly, the optimal feature subsets are determined by LPP. Secondly, the results of activity recognition are classified by RF ensemble classifier. Compared with other methods, the method uses the significantly less number of features, and the over-all accuracy has been increased.

Key words: human activity recognition；random forest, sparse locality preserving projections, smartphone