Training an EMG-Based Machine Learning Model to Classify Hand Gestures in a Spatial Virtual Reality Environment
Term
4. term
Education
Publication year
2025
Submitted on
2025-05-26
Pages
14
Abstract
This study explored the use of an electromyography (EMG) based machine learning model to classify hand gestures in a spatial virtual reality (VR) environment. A total of 18 participants participated in the evaluation. The EMG signals were recorded from the fore- arm of each participant, and they were used to train intra-subject classification models to predict the movement of a hand prosthetic. The results showed a significant gap between the offline and online performance of the trained model, with macro F1 scores averag- ing 0.86, while a notable average performance drop was observed during the online tests, where the macro F1 score fell to 0.52. Indi- vidual participants achieved more satisfactory scores, suggesting the presence of individual differences that may be influenced by various contributing factors. The results show promising steps to- wards training and testing a machine learning algorithm to control a hand model with EMG signals in VR. With further development, this approach has the potential to support amputees in training a prosthetic hand within a spatial VR environment prior to receiving the physical device
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