2024/09/24 by Shriram Tallam Puranam Raghu, Raghu, Shriram Tallam Puranam, Dawn MacIsaac +3
Engineering · #FOS: Electrical engineering #Muscle activation and electromyography studies #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2409.16015
openalex publication_date 2024/09/24 · openalex created_date 2024/10/26 · openalex updated_date 2026/07/28
Pattern recognition-based myoelectric control is traditionally trained with static or ramp contractions, but this fails to capture the dynamic nature of real-world movements. This study investigated the benefits of training classifiers with continuous dynamic data, encompassing transitions between various movement classes. We employed both conventional (LDA) and deep learning (LSTM) classifiers, comparing their performance when trained with ramp data, continuous dynamic data, and continuous dynamic data augmented with a self-supervised learning technique (VICReg). An online Fitts' Law test with 20 participants evaluated the usability and effectiveness of each classifier. Results demonstrate that temporal models, particularly LSTMs trained with continuous dynamic data, significantly outperformed traditional approaches. Furthermore, VICReg pre-training led to additional improvements in online performance and user experience. Qualitative feedback highlighted the importance of smooth, jitter-free control and consistent performance across movement classes. These findings underscore the potential of continuous dynamic data and self-supervised learning for advancing sEMG-PR-based myoelectric control, paving the way for more intuitive and user-friendly prosthetic devices.