123 citations · 123 across the 4 of their papers we have counts for
4 papers
Context Informed Incremental Learning Improves Myoelectric Control Performance in Virtual Reality Object Manipulation Tasks
Gabriel Gagné, Anisha Azad, Thomas Labbé +5
Electromyography (EMG)-based gesture recognition is a promising approach for designing intuitive human-computer interfaces. However, while these systems typically perform well in c…
A Flexible and Modular Body-Machine Interface for Individuals Living with Severe Disabilities
Cheikh Latyr Fall, Ulysse Côté-Allard, Quentin Mascret +4
This paper presents a control interface to translate the residual body motions of individuals living with severe disabilities, into control commands for body-machine interaction. A…
A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition
Ulysse Côté-Allard, Gabriel Gagnon-Turcotte, Angkoon Phinyomark +4
Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the offline accuracy reported in the literature and the real-time usability of a cla…
Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features
Ulysse Côté-Allard, Evan Campbell, Angkoon Phinyomark +3
The research in myoelectric control systems primarily focuses on extracting discriminative representations from the electromyographic (EMG) signal by designing handcrafted features…