Control of a robot manipulator using surface electromyography signals

The subject of the study is the methods of controlling a robotic manipulator based on surface electromyography (EMG) signals using individualized normalization and classification of gestures by a convolutional neural network (CNN). The aim of the work is to create an effective control system that combines pre-normalization of EMG signals and deep learning to increase the accuracy of gesture recognition and stability in real time. For this purpose, the following were used: signal collection using the Myo Armband, pre-processing (min-max and zero-mean), signal conversion into images through a sliding window, CNN training (ResNet, Adam optimization) and comparison with SVM and Random Forest models. The results obtained showed a classification accuracy of 97.27% in the test environment and 91.71% in real time. The CNN model outperformed traditional methods by 18–19%, and zero-mean increased the accuracy by 2.34% compared to min-max normalization. The system remained stable even with variations in the bracelet position due to individual normalization. Conclusions: the proposed system demonstrates high accuracy, reliability and adaptability in real time. The scientific novelty lies in the combination of individualized normalization of EMG signals with ResNet, which provides stability and exceeds the accuracy of traditional algorithms. In the future, it is planned to expand the set of gestures, study more complex conditions and optimize neural networks for embedded systems.