collaborators

5 papers

eess.SP2020

A Comparison of Amputee and Able-Bodied Inter-Subject Variability in Myoelectric Control

Evan Campbell, Jason Chang, Angkoon Phinyomark +1

Despite decades of research and development of pattern recognition approaches, the clinical usability of myoelectriccontrolled prostheses is still limited. One of the main issues i…

eess.SP2020

Differences in Perspective on Inertial Measurement Unit Sensor Integration in Myoelectric Control

Evan Campbell, Angkoon Phinyomark, Erik Scheme

Recent human computer-interaction (HCI) studies using electromyography (EMG) and inertial measurement units (IMUs) for upper-limb gesture recognition have claimed that inertial mea…

cs.HC2019

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…

cs.LG2019

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…

eess.SP2019

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…