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.
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2018, Vol. 31 
