A Gait Phase Detection Method in Complex Environment Based on DTW-Mean Templates

Gait phase detection is essential for the wearable powered exoskeletons. This paper proposes an online gait phase detection method based on dynamic time warping mean (DTW-MEAN) templates using ground contact forces (GCFs). There are two important methods of gait phase detection, which are the template matching and statistical method. Template matching methods such as DTW are robust and have been widely used in the field of gait phase detection. However, the template matching is a kind of method based on distance measure, and the gait phase detection will ignore the coupling connections between the gait sequences; on the contrary, the statistical method can fuse these connections well. In this paper, a statistical mean method based on DTW is proposed to solve the problem of recognizing phases between gait sequences that are not correctly detected by single template methods. We tested our approach in complex environment (such as different terrains, different payloads, and different speeds) and gained over 95% accuracy and time difference below 25ms. Our proposed approach obtained better detection results with the advantage of no need to retrain templates.