2022
Loi, Iliana; Grammatikaki, Angeliki; Tsinganos, Panagiotis; Bozkir, Efe; Ampeliotis, Dimitris; Moustakas, Konstantinos; Kasneci, Enkelejda; Skodras, Athanassios
Proportional Myoelectric Control in a Virtual Reality Environment Proceedings Article
In: 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), IEEE, Nafplio, Greece, 2022.
Abstract | Links | BibTeX | Tags: Deep learning, hand gesture recognition, proportional myoelectric control, semg, virtual reality
@inproceedings{nokey,
title = {Proportional Myoelectric Control in a Virtual Reality Environment},
author = {Iliana Loi and Angeliki Grammatikaki and Panagiotis Tsinganos and Efe Bozkir and Dimitris Ampeliotis and Konstantinos Moustakas and Enkelejda Kasneci and Athanassios Skodras},
doi = {10.1109/IVMSP54334.2022.9816252},
year = {2022},
date = {2022-07-11},
urldate = {2022-07-11},
booktitle = {2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)},
publisher = {IEEE},
address = {Nafplio, Greece},
abstract = {Translating input modalities such as hand interactions, speech, and eye tracking in virtual reality offers an immersive user experience. Especially, it is crucial to track the user’s hand gestures, since they can help in translating user intentions into actions in virtual environments. In this work, we developed a virtual reality application which incorporates electromyography-based deep learning methods for recognizing and estimating hand movements in an online fashion. Our application automates all user controls, providing an immense potential for rehabilitation purposes.},
keywords = {Deep learning, hand gesture recognition, proportional myoelectric control, semg, virtual reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Translating input modalities such as hand interactions, speech, and eye tracking in virtual reality offers an immersive user experience. Especially, it is crucial to track the user’s hand gestures, since they can help in translating user intentions into actions in virtual environments. In this work, we developed a virtual reality application which incorporates electromyography-based deep learning methods for recognizing and estimating hand movements in an online fashion. Our application automates all user controls, providing an immense potential for rehabilitation purposes.