CAJ | 학술논문

基于体感网对人体动作进行识别的很多研究都是针对单人动作，很少有研究讨论双人交互动作的识别。针对双人交互动作中两人肢体行为的特点，提出了一种隐马尔可夫模型和马尔可夫逻辑网相结合的方法。其中，单人原子行为通过建立隐马尔可夫模型来进行识别，在两人交互行为的语义建模中，建立一阶逻辑知识库，并通过训练马尔可夫逻辑网来最终实现两人交互行为的决策。实验结果表明，与基于特征层数据融合的一些方法相比，该方法获得了更高的识别精度，能够有效地识别出双人交互动作。
기우체감망대인체동작진행식별적흔다연구도시침대단인동작，흔소유연구토론쌍인교호동작적식별。침대쌍인교호동작중량인지체행위적특점，제출료일충은마이가부모형화마이가부라집망상결합적방법。기중，단인원자행위통과건립은마이가부모형래진행식별，재량인교호행위적어의건모중，건립일계라집지식고，병통과훈련마이가부라집망래최종실현량인교호행위적결책。실험결과표명，여기우특정층수거융합적일사방법상비，해방법획득료경고적식별정도，능구유효지식별출쌍인교호동작。
Existing work in human activity recognition based on Body Sensor Networks(BSN)mainly focuses on recog-nizing 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.