The recognition of lower limb movement has a wide range of applications in rehabilitation training, wearable exoskeleton control, and human activity monitoring. Surface electromyography (sEMG) signals can directly reflect the intention of human movement and can be used as the source of lower limb movement recognition. Literature reports have shown that extracting features from sEMG signals is the core of human movement recognition based on sEMG signals. However, how to effectively extract features from the sEMG signal of the lower limbs affected by body gravity is a difficult problem for the recognition of lower limb movement based on the sEMG signal.

The main objective of this paper is to propose an efficient lower limb movement recognition model based on sEMG signals to accurately recognize the four lower limb movements.

We proposed a novel method of lower limbs movements recognition based on tunable Q-factor wavelet transform (TQWT) and Kraskov entropy (KrEn). Firstly, the sEMG signals of four different lower limb movements from twenty subjects were recorded by seven wearable sEMG signal sensors, and the recorded sEMG signals were denoised by multi-scale principal component analysis (MSPCA). Then, the denoised sEMG signal is decomposed into multiple sub-band signals by TQWT and the KrEn feature is extracted from each sub-band signal. Next, the representative features are selected from the extracted KrEn features by the minimum redundancy maximum relevance (mRMR) feature selection method. Finally, the four lower limb movements are recognized by three machine learning classifiers. Besides, to improve the recognition performance, a majority voting (MV) technology is proposed for the post-processing of decision flow. Experimental results show that the combination of TQWT, KrEn, and MV technology achieved the average recognition accuracy of 98.42% using the linear discriminant analysis (LDA) classifier.

The method proposed in this paper can recognize lower limb movements with high accuracy. Compared with existing methods, this method is more advanced and accurate, indicating that it has great application potential in rehabilitation training, wearable exoskeleton control, and daily activity monitoring.