3 papers
eess.SP2024
Self-Supervised Representation Learning with Augmentations of Continuous Training Data Improves the Feel and Performance of Myoelectric Control
Shriram Tallam Puranam Raghu, Dawn MacIsaac, Erik Scheme
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…
eess.SP2024
Decision-change Informed Rejection Improves Robustness in Pattern Recognition-based Myoelectric Control
Shriram Tallam Puranam Raghu, Dawn MacIsaac, Erik Scheme
Post-processing techniques have been shown to improve the quality of the decision stream generated by classifiers used in pattern-recognition-based myoelectric control. However, th…
eess.SP2024
Self-Supervised Learning via VICReg Enables Training of EMG Pattern Recognition Using Continuous Data with Unclear Labels
Shriram Tallam Puranam Raghu, Dawn T. MacIsaac, Erik J. Scheme
In this study, we investigate the application of self-supervised learning via pre-trained Long Short-Term Memory (LSTM) networks for training surface electromyography pattern recog…