Knowledge Transfer to Improve sEMG Simultaneous Proportional Movement Detection: A Data Transformation Approach.

Simultaneous proportional detection (SPD), based on surface electromyography (sEMG) signals, is a promising approach that achieves higher performance when trained on complex movements. However, collecting such training data is time-consuming for the user. To enable real-world applications, it is crucial to balance precision and ease of use. This study proposes a data transformation approach to enhance detection performance by transferring knowledge from a pre-existing dataset of complex movements to a simpler movement dataset. So, instead of asking a user to perform a variety of movements each session, a pre-recorded set of complex wrist movements data, including star-shaped trajectories, is used to add richness to a simpler set of wrist movement data requested from the user to perform movements only in X and Y. A linear transformation model is proposed to transfer movement knowledge between datasets, improving accuracy while maintaining the advantages of transfer learning. Experimental results confirm that the proposed knowledge transformation model improves R2 performances by 5-10%. Additionally, the approach reduces data acquisition time, enhancing practicality for real-world applications in human-machine interaction, while being effective for both inter-session and inter-subject scenarios.