The deep learning gesture recognition based on surface electromyography plays an increasingly important role in human-computer interaction. In order to ensure the high accuracy of deep learning in multistate muscle action recognition and ensure that the training model can be applied in the embedded chip with small storage space, this paper presents a feature model construction and optimization method based on multichannel sEMG amplification unit. The feature model is established by using multidimensional sequential sEMG images by combining convolutional neural network and long-term memory network to solve the problem of multistate sEMG signal recognition. The experimental results show that under the same network structure, the sEMG signal with fast Fourier transform and root mean square as feature data processing has a good recognition rate, and the recognition accuracy of complex gestures is 91.40%, with the size of 1 MB. The model can still control the artificial hand accurately when the model is small and the precision is high.

Background: Surface electromyography-based gesture recognition and prosthetic hand control using deep learning have become increasingly significant in the field of human-computer interaction. Objective: This study aims to enhance the control of prosthetic hands driven by complex gestures, addressing the challenge of low-resolution gesture differentiation caused by the coupling and superposition of surface electromyography signals in deep learning models. Method: We propose a deep learning-based framework for the recognition of complex surface electromyography signals, utilizing a multi-pathway approach to acquire raw surface electromyography signals, process them in the time-frequency domain, and extract features using multi-scale convolutional networks. The processed surface electromyography features are then analyzed in parallel to enhance accuracy. Results: This method effectively processes multiple signals concurrently and extracts diverse feature sets. By collecting data from six channels, it achieves an 88.56% recognition rate for 16 complex hand gestures, enabling control of 10 distinct prosthetic hand movements. Conclusion: By leveraging multi-dimensional continuous surface electromyography images, we have developed a feature model that resolves the issues of signal coupling and superposition in multi-channel surface electromyography data, allowing for precise control of prosthetic hand movements.

As a kind of lower-limb motor assistance device, the intelligent walking aid robot plays an essential role in helping people with lower-limb diseases to carry out rehabilitation walking training. In order to enhance the safety performance of the lower-limb walking aid robot, this study proposes a deep vision-based abnormal lower-limb gait prediction model construction method for the problem of abnormal gait prediction of patients’ lower limbs. The point cloud depth vision technique is used to acquire lower-limb motion data, and a multi-posture angular prediction model is trained using long and short-term memory networks to build a model of the user’s lower-limb posture characteristics during normal walking as well as a real-time lower-limb motion prediction model. The experimental results indicate that the proposed lower-limb abnormal behavior prediction model is able to achieve a 97.4% prediction rate of abnormal lower-limb movements within 150 ms. Additionally, the model demonstrates strong generalization ability in practical applications. This paper proposes further ideas to enhance the safety performance of lower-limb rehabilitation robot use for patients with lower-limb disabilities.