15 citations · 25 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Towards Robust and Interpretable EMG-based Hand Gesture Recognition using Deep Metric Meta Learning
Simon Tam, Shriram Tallam Puranam Raghu, Étienne Buteau +4
Current electromyography (EMG) pattern recognition (PR) models have been shown to generalize poorly in unconstrained environments, setting back their adoption in applications such…
On-Demand Myoelectric Control Using Wake Gestures to Eliminate False Activations During Activities of Daily Living
Ethan Eddy, Evan Campbell, Scott Bateman +1
While myoelectric control has recently become a focus of increased research as a possible flexible hands-free input modality, current control approaches are prone to inadvertent fa…