Robotic Gait Parameter Extraction with Multi-Scale Temporal Attention Enhancement Network

Spatiotemporal gait parameters play a crucial role in assessing the rehabilitation progress of patients with lower-limb impairments. However, existing gait parameter extraction methods heavily rely on wearable devices or high-precision motion capture systems, limiting their applicability in everyday environments. To overcome this limitation, we propose a novel gait parameter extraction system in which a Red–RGB-D camera, mounted on a mobile robot, tracks human walking motion to capture lower-limb depth information. The system employs a RGB-D camera to capture lower-limb motion data and introduces the Multi-Scale Temporal Attention Enhancement Network(MSTAE-Net) for gait parameter extraction. This model effectively mitigates the challenges of insufficient temporal feature representation and difficulty in capturing abrupt feature variations at gait phase transitions. Additionally, it segments gait cycles and extracts spatiotemporal gait parameters accordingly. Experiments were conducted on four healthy subjects and six patients, comparing the proposed method against four classical classification algorithms. The results demonstrate that our approach achieves an average gait phase classification accuracy of 96.56%, outperforming BiLSTM by 5.4 %. For spatial gait parameters, the average error compared to footprint-based measurements is 0.02 m; for temporal gait parameters, the average error is 0.1 s compared to video frame-by-frame annotated gait events. The experimental results validate the proposed method’s accuracy and robustness, demonstrating its feasibility and efficiency for non-wearable gait parameter extraction and its potential in clinical rehabilitation assessment and daily monitoring.