Subject-Independent sEMG-Based Prosthetic Control Using MAMBA2 with Domain Adaptation

Kihyun Kim, Jiyeon Kang

Abstract: Integrating functional wrist articulation in prosthetic robot arms is crucial for enhancing natural movement and reducing compensatory upper limb motions. However, two significant challenges remain in electromyography (sEMG)-based prosthetic control: (1) real-time processing via efficient model design and (2) cross-subject generalization to address the individual variability in muscle signals. This study employs the MAMBA2 architecture to address the first challenge, leveraging Structured State Space Models (SSM) for efficient long-sequence inference. This enables real-time control with minimal computational overhead, making it well-suited for prosthetic robot arm applications. To tackle the second challenge, we implement a Representation Subspace Distance (RSD)-based Unsupervised Domain Adaptation (UDA), which preserves feature scale while aligning inter-subject variations, mitigating domain shift effects, and improving subject-independent wrist movement estimation. The model is trained on the Ninapro DB2 dataset, utilizing multi-channel sEMG signals and corresponding wrist kinematics. Evaluation results demonstrate that the MAMBA architecture outperforms conventional recurrent neural networks, achieving lower Mean Squared Error (MSE) and higher R² values, with the Attention variant exhibiting the best prediction performance. Furthermore, this study highlights that the proposed UDA approach, combined with RSD-based alignment, significantly enhances cross-subject performance, reducing the need for extensive calibration. By enabling real-time processing through a computationally efficient model structure and effectively addressing cross-subject variability, this study contributes to developing a more reliable and generalizable sEMG-based robotic prosthesis controller, ultimately improving its applicability across diverse individuals.

External IDs:dblp:conf/iros/KimK25