Research on segmentation and recognition of human daily action sequence based on HMMs and SVM
WU Dong-hu · Dalian Ligong Daxue xuebao · 2015
With the refinement of the study of the micro-electro-mechanical system(MEMS),the application of body sensor networks(BSN)has developed rapidly in the field of medical care.Human motion analysis and recognition are challenging research topics in the BSN.An approach of the dynamic hidden Markov models(HMMs)is proposed to segment the time series of the activities based on BSN.A method of the precision measurement is used to test the approach of the segmentation.The experimental results show that the proposed approach is prior to the LIR and Top-Down methods and the segmentation precision of the dynamic HMMs is above 80%.The features of the data obtained from segmentation,such as mean,variance,etc.are extracted.The results of the recognition by support vector machine(SVM)show the robustness of the proposed segmentation method.The mean recognition accuracy is about 89%,which is near the manual segmentation.