关键词: 体感网, 双人交互动作, 隐马尔可夫模型, 数据融合, 一阶逻辑, 马尔可夫逻辑网

Abstract: Existing work in human activity recognition based on Body Sensor Networks（BSN） mainly focuses on recognizing single-user activities and lacks of discussions about two-body interactive activities. A new hierarchical recognition framework which consists of Hidden Markov Model（HMM） and Markov Logic Network（MLN） is proposed according to the characteristics of two-body interactive actions. The primitive actions of a single person are recognized by using Hidden Markov Model, and the final decision of interactive actions is made by constructing first-order logic knowledge base and employing MLN. Experimental results on the interaction dataset show that the proposed method can achieve a higher accuracy compared to other methods in activity recognition of two-body interactions.

Key words: Body Sensor Networks（BSN）, two-body interactive activities, Hidden Markov Model（HMM）, data fusion, first-order logic, Markov Logic Network（MLC）