Lower limb ankle exoskeletons have been used to improve walking efficiency and assist the elderly and patients with motor dysfunction in daily activities or rehabilitation training, while the assistance patterns may influence the wearer’s lower limb muscle activities and coordination patterns. In this paper, we aim to evaluate the effects of different ankle exoskeleton assistance patterns on wearer’s lower limb muscle activities and coordination patterns. A tethered ankle exoskeleton with nine assistance patterns that combined with differenet actuation timing values and torque magnitude levels was used to assist human walking. Lower limb muscle surface electromyography signals were collected from 7 participants walking on a treadmill at a speed of 1.25 m/s. Results showed that the soleus muscle activities were significantly reduced during assisted walking. In one assistance pattern with peak time in 49% of stride and peak torque at 0.7 N·m/kg, the soleus muscle activity was decreased by (38.5 ± 10.8)%. Compared with actuation timing, the assistance torque magnitude had a more significant influence on soleus muscle activity. In all assistance patterns, the eight lower limb muscle activities could be decomposed to five basic muscle synergies. The muscle synergies changed little under assistance with appropriate actuation timing and torque magnitude. Besides, co-contraction indexs of soleus and tibialis anterior, rectus femoris and semitendinosus under exoskeleton assistance were higher than normal walking. Our results are expected to help to understand how healthy wearers adjust their neuromuscular control mechanisms to adapt to different exoskeleton assistance patterns, and provide reference to select appropriate assistance to improve walking efficiency.
The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography (sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal. Then, we build a muscle fatigue state recognition model based on long short-term memory (LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.
An in-depth understanding of the mechanism of lower extremity muscle coordination during walking is the key to improving the efficacy of gait rehabilitation in patients with neuromuscular dysfunction. This paper investigates the effect of changes in walking speed on lower extremity muscle synergy patterns and muscle functional networks. Eight healthy subjects were recruited to perform walking tasks on a treadmill at three different speeds, and the surface electromyographic signals (sEMG) of eight muscles of the right lower limb were collected synchronously. The non-negative matrix factorization (NNMF) method was used to extract muscle synergy patterns, the mutual information (MI) method was used to construct the alpha frequency band (8–13 Hz), beta frequency band (14–30 Hz) and gamma frequency band (31–60 Hz) muscle functional network, and complex network analysis methods were introduced to quantify the differences between different networks. Muscle synergy analysis extracted 5 muscle synergy patterns, and changes in walking speed did not change the number of muscle synergy, but resulted in changes in muscle weights. Muscle network analysis found that at the same speed, high-frequency bands have lower global efficiency and clustering coefficients. As walking speed increased, the strength of connections between local muscles also increased. The results show that there are different muscle synergy patterns and muscle function networks in different walking speeds. This study provides a new perspective for exploring the mechanism of muscle coordination at different walking speeds, and is expected to provide theoretical support for the evaluation of gait function in patients with neuromuscular dysfunction.