Fine-tuning Myoelectric Control through Reinforcement Learning in a Game Environment
Artikel i vetenskaplig tidskrift, 2025

Objective: Enhancing the reliability of myoelectric controllers that decode motor intent is a pressing challenge in the field of bionic prosthetics. State-of-the-art research has mostly focused on Supervised Learning (SL) techniques to tackle this problem. However, obtaining high-quality labeled data that accurately represents muscle activity during daily usage remains difficult. We investigate the potential of Reinforcement Learning (RL) to further improve the decoding of human motion intent by incorporating usage-based data.
Methods: The starting point of our method is a SL control policy, pretrained on a static recording of electromyographic (EMG) ground truth data. We then apply RL to fine-tune the pretrained classifier with dynamic EMG data obtained during interaction with a game environment developed for this work. We conducted real-time experiments to evaluate our approach and achieved significant improvements in human-in-the-loop performance.
Results: The method effectively predicts simultaneous finger movements, leading to a two-fold increase in decoding accuracy during gameplay and a 39% improvement in a separate motion test.
Conclusion: By employing RL and incorporating usage-based EMG data during fine-tuning, our method achieves significant improvements in accuracy and robustness. Significance: These results showcase the potential of RL for enhancing the reliability of myoelectric controllers, which is of particular importance for advanced bionic limbs. See our project page for visual demonstrations: https://sites.google.com/view/bionic-limb-rl.

Reinforcement learning

Electromyography

Human computer interaction

Deep Learning

Prosthetic limbs

Författare

Kilian Tamino Freitag

Chalmers, Elektroteknik, System- och reglerteknik

Yiannis Karayiannidis

Lunds universitet

Jan Zbinden

Chalmers, Elektroteknik, System- och reglerteknik

Rita Laezza

Chalmers, Elektroteknik, System- och reglerteknik

IEEE Transactions on Biomedical Engineering

0018-9294 (ISSN) 15582531 (eISSN)

Vol. In Press

Ämneskategorier (SSIF 2025)

Robotik och automation

Datavetenskap (datalogi)

DOI

10.1109/TBME.2025.3578855

Mer information

Senast uppdaterat

2025-06-25