LAUSR

Gait phase contains rich kinematic information of lower limbs, which has great reference significance for rehabilitation medicine, assistive design, and identity recognition. This research presents a wearable gait phase segmentation method based on lower limb motion capture (MoCap) technique. In our method, a body sensor network (BSN) covering the whole lower limbs was established to capture the motion data of human gait, and a 3-D lower limb dynamic model is created to reconstruct lower limb movements through multisensor data fusion. Six gait events are labeled by the lower limb dynamic model. Then, a deep classification network combining temporal convolutional network (TCN) and long-short-term memory (LSTM) is proposed to segment the six gait phases as pattern classification. In addition, different sensor combinations for gait phase segmentation were also evaluated to select optimal sensor layouts. Detection performance is evaluated using metrics of accuracy, specificity, recall, and F1 score, and the averaged performance values are 98.9%, 98.9%, 98.8%, and 98.9%, respectively. The overall experimental results demonstrate that our proposed method can well address the issue of gait phase segmentation and provide spatial–temporal parameters for further gait analysis.