Effects on Hand Gesture Recognition Accuracy after Severe Cranial Trauma

This study examines the impact of severe cranial trauma on the accuracy of Hand Gesture Recognition (HGR) models based on electromyography (EMG) signals. The study focuses on a single subject, who suffered left-sided hemiplegia, and exhibits limited mobility and spasticity in the left hand. EMG samples corresponding to six hand gestures from his both forearms were collected using the Myo Armband. The 2023HGR5-CNN-LSTM HGR model was used to evaluate the recognition accuracy of these samples. Results showed significantly lower HGR performance in the left side (10.67% recognition accuracy) compared to the right side (56% recognition accuracy). As the selected model was trained with EMG data only from right arms, a channel mapping to adapt the EMG signals from the left side was also developed. Unfortunately, the channel mapping implemented did not improve the recognition accuracy (9.33%). These findings highlight the challenges in achieving high performance HGR for individuals with cranial injuries. It might also point out that developing myoelectric prostheses using datasets from unaffected individuals may be inadequate for training robust models.