Hand-gesture recognition based on sEMG using deep learning architectures

Abstract

Hand gesture recognition is a key area in non-verbal communication, enabling intuitive and touchless interaction between individuals and digital systems. As non-verbal communication plays a vital role in human interaction, hand gesture recognition systems can improve accessibility and increase communication efficiency. Over time, hand-gesture recognition has been considered more important, and this can be achieved by using surface electromyography (sEMG) signals. SEMG is a type of electromyography (EMG) procedure where the signals are recorded on the skin surface rather than within the muscle. The main problem with sEMG signals is that there are several physiological processes in the skeletal muscles underlying their generation. This is the main reason gesture recognition using an sEMG is a non-trivial task. Noise is also a contributing factor to the problem with sEMG signals. A dataset is created with 30 participants and 10 communication hand gestures that is then split between training, validation, and testing. To create the dataset, sEMG signals are collected via a controlled experiment using a hand gesture recording device such as the Myo armband. This study explores deep learning algorithms for hand gesture classification and evaluation. The implementation of real-time hand gesture recognition is studied using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM). The research examines how these models can adapt to dynamic movement and positioning, which may affect recognition accuracy. A key objective of this study is to provide a reliable and efficient manner in predicting hand gestures, making it applicable in various fields. One of those fields is verbal communication throughout the day. Experimental results demonstrate the reliability of integrating gesture recognition into an information system that predicts hand gestures in real time, offering improved accessibility and communication support. By optimising feature selection and model performance, this research contributes valuable insights for advancing gesture-based predictive systems, by achieving a net result of 80.67% for the recognition of 10 hand gestures. This dissertation enhances the field of non-verbal communication through gesture recognition, paving the way for more sophisticated and accessible interaction technologies.