With an increased frequency of accidents and birth defects issues, such as congenital amputation, the need for improved prosthetic devices increases. In this work, we present a rigid prosthetic system fabricated by 3D printing and a deep learning network to classify hand movements with surface electromyography (sEMG) signals of forearm muscles. The model, called NeuroAttend-EMG, comprises a CNN, a BiLSTM, and a self-attention module, which runs on edge devices like the Jetson Nano. With a parameter count of 1.2 million and a computational complexity of 0.0015 GFLOPs, the model achieved 98.58% and 97.34 % classification accuracies on two distinct sEMG-based tabular signal datasets. Four time-domain features were selected and evaluated for feature extraction on both datasets, ensuring a robust representation of the EMG signals. Furthermore, interpretable AI methods like Shapley Additive Explanations (SHAP) were used to recognize the most important features. A 3D-printed hand prosthetic was created, which performed some of the classification movements based on the unseen sensor data provided within the Jetson Nano board. The study proves that it is possible to apply the light deep learning to the classification of hand movements in prosthetics for real-time control, striking a balance between efficiency and accuracy in edge computing. The study focuses on the field of assistive robotics from optimized deep learning-based prosthetics systems, enabling affordable, accurate movement classification on low-powered devices.
Karim et al. (Sun,) studied this question.