Gait recognition based on sEMG signal using progressive feature selection method

Gait recognition based on surface electromyography (sEMG) signals has many applications in exoskeleton control. However, due to the irrelevance and redundancy of its features, how to extract features effectively and improve the recognition accuracy is a hotspot of current research.

This study proposes a progressive feature selection (PFS) gait recognition method based on sEMG. First, to solve the problem of inaccurate gait description, the stereo modelling projection and 3D dynamic capture are fused to capture the time and frequency domain features derived from the four muscles of the human lower limb according to the gait phase. Then, to address the problem of poor gait classification accuracy, a progressive feature combination optimization is performed based on the fitness evaluation to preserve the key information embedded in the features while eliminating features that contribute less to the model accuracy. Therefore, model accuracy is improved by determining the best combination of features.

The progressive feature selection method shows considerable performance in sEMG-based gait recognition, with the average accuracy of 98.54 % and the median accuracy of 98.67 %.

In order to verify the effectiveness of the proposed algorithm more comprehensively, the practical experimental dataset and the publicly available SIAT-LLMD dataset are adopted respectively. Compared with the state-of-the-art methods, the gait recognition accuracy of the proposed PFS algorithm can reach 98.91 % and 98.54 %.

The proposed PFS gait recognition method can significantly reduce unnecessary features, thus improving the recognition accuracy and safety of lower limb exoskeleton robots.