A Gait Pattern Classification Method Based on CLSTM

Gait classification is an effective method for the assessment of human motion energy consumption,in which the key issue is to improve its classification accuracy and decrease the dependence on statistic features.Aiming at the shortcoming of the traditional gait classification methods in classifying subdivided gaits,this paper proposes a CLSTM method by considering the continuity of the gait and the signal correlation among different axes.By means of CNN convolution operation,this proposed method can extract the gait features by calculating the gait data among multi-axis.Besides,the present method utilizes the LSTM-based gait time series model to learn long-term dependent relation on gait features in the time dimension.Finally,it is illustrated via experiment on USC-HAR datasets that the proposed method can extract gait sequence features and effectively utilize the characteristics of gait time-series to raise classification accuracy in 11 gaits pattern.