Abstract Foot pressure information is utilized to identify human gait in the study of walking. However, the bipedal pressure signal collected by a multi-sensor array has the problems of high redundancy, weak correlation and strong noise interference. To identify the movement states of human lower limbs, singular value decomposition is adopted to fuse multi-source observation data of foot pressure and extract the characteristic signal of gait motion. Then, the characteristic signal is expanded into a gait information subspace, and the feature points are clustered based on fuzzy C-means clustering algorithm. Since the feature points and the signal sampling sequence are mapped one by one, the gait movement process is divided by the clustering result in the time domain. Experimental results show that five typical movement states of human lower limbs can be effectively identified by the proposed method.
Fund:Supported by National Natural Science Foundation of China (No.61741101), Natural Science Foundation of Anhui Province(No.1608085QF154), Key Project of Science and Technology of Anhui Province(No.1604a0902125), Scientific Research Foundation of Anhui Polytechnic University for the Introduction of Talents(No.2015YQQ005)
Corresponding Authors:
XU Dezhang, Ph.D., professor. His research interests include robot information perception, signal acquisition and application.
About author:: GAO Lijun, master student. His research interests include robot information perception. WANG Buyun, Ph.D., lecturer. His research interests include robot information perception.
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2018, Vol. 31 
