Enhancing surface electromyographic signal recognition accuracy for trans-radial amputees using broad learning systems.

Gesture recognition based on surface electromyography (sEMG) plays a crucial role in human-computer interaction. By analyzing sEMG signals generated from residual forearm muscle activity in trans-radial amputees, it is possible to predict their hand movement intentions, enabling the control of myoelectric prostheses. Previous studies on gesture classification for trans-radial amputees have shown that as the number of hand movement types increases, the accuracy of gesture classification significantly decreases. To this end, this paper proposed a novel approach that integrates image feature flattening (IFF) with broad learning system (BLS). The IFF method mapped data features into a three-dimensional image, which was then converted to grayscale and flattened into a one-dimensional vector. Finally, it was used as input to the BLS network for precise gesture classification. The proposed method was validated on 49 hand movement data from radial amputees in the Ninapro DB3 dataset. The results showed that the method not only significantly reduced classification time but also achieved a gesture recognition accuracy of up to 98.1%, demonstrating its strong potential for application in gesture recognition for trans-radial amputees.