A Novel Gesture Recognition Network based on LSTM

Surface electromyography (sEMG) is a rich source of physiological data that reflects human movement intentions. The recognition of gestures through sEMG has garnered significant interest in the realms of human-computer interaction and rehabilitation. Currently, the majority of research on gesture recognition utilizing sEMG signals relies on discrete segmentation techniques, often overlooking the nuances of continuous and natural movements. A novel gesture recognition network based on LSTM for recognizing gestures from sEMG signals is prpopsed. The sEMG sensors are strategically positioned based on anatomical and muscular functions to optimize the capture of relevant physiological signals. In this study, finger curvature is employed to characterize gesture states, allowing each gesture at any given moment to be represented by a collection of varying finger curvatures, thereby enabling continuous gesture recognition. The experimental results indicate that this method significantly enhances the ability to mine representations from sEMG signals, offering valuable insights for deep learning models focused on human gesture recognition.