Research on Enhanced Gait Phase Segmentation Based on Multimodal Spatiotemporal Information Fusion

Gait phase segmentation, pivotal for understanding lower limb motion, finds applications in diverse fields like medicine and sports. While existing method often struggle with accuracy and adaptability in real-world settings, this study presents a novel methodology employing particle filters for precise lower limb motion capture (MoCap) utilizing inertial sensors, which can be used in more everyday environments and in a wider range of applications over a longer period of time. The innovative approach adeptly tracks walking movements, labeling six gait phases via skeleton reconstruction facilitated by the MoCap algorithm. Subsequently, we propose a neural network architecture amalgamating temporal convolutional network (TCN), graph convolutional network (GCN), and long short-term memory (LSTM). This architecture integrates raw data from inertial sensors with joint angles derived from reconstructed motion, achieving accurate segmentation of the six gait phases. Experimental validation compares the MoCap algorithm against an optical motion capture system, and the neural network’s performance against state-of-the-art methods. Results demonstrate our method’s superior accuracy of 96.94%, highlighting its efficacy in addressing gait phase segmentation challenges and propelling advancements in gait analysis.