Towards Efficient Neural Decoder for Dexterous Finger Force Predictions.
Journal:
IEEE transactions on bio-medical engineering
Published Date:
Jun 1, 2024
Abstract
OBJECTIVE: Dexterous control of robot hands requires a robust neural-machine interface capable of accurately decoding multiple finger movements. Existing studies primarily focus on single-finger movement or rely heavily on multi-finger data for decoder training, which requires large datasets and high computation demand. In this study, we investigated the feasibility of using limited single-finger surface electromyogram (sEMG) data to train a neural decoder capable of predicting the forces of unseen multi-finger combinations.