123 citations · 192 across the 5 of their papers we have counts for
5 papers
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…
Unsupervised Domain Adversarial Self-Calibration for Electromyographic-based Gesture Recognition
Ulysse Côté-Allard, Gabriel Gagnon-Turcotte, Angkoon Phinyomark +4
Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance ov…
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…
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
Ulysse Côté-Allard, Cheikh Latyr Fall, Alexandre Drouin +5
In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of…