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of 257
pro vyhledávání: '"SCHEME, ERIK"'
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 study investigated the benefits of training classifiers with continuou
Externí odkaz:
http://arxiv.org/abs/2409.16015
Publikováno v:
Biomedical Signal Processing and Control, vol. 71, p. 103134, Jan. 2022
Despite continued efforts to improve classification accuracy, it has been reported that offline accuracy is a poor indicator of the usability of pattern recognition-based myoelectric control. One potential source of this disparity is the existence of
Externí odkaz:
http://arxiv.org/abs/2409.14172
Publikováno v:
S. T. P. Raghu, D. MacIsaac and E. Scheme, IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 12, pp. 6051-6061, Dec. 2023
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, these techniques have largely been tested individually and on well-behav
Externí odkaz:
http://arxiv.org/abs/2409.14169
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 recognition models (sEMG-PR) using dynamic data with transitions. While lab
Externí odkaz:
http://arxiv.org/abs/2409.11632
Autor:
Tam, Simon, Raghu, Shriram Tallam Puranam, Buteau, Étienne, Scheme, Erik, Boukadoum, Mounir, Campeau-Lecours, Alexandre, Gosselin, Benoit
Current electromyography (EMG) pattern recognition (PR) models have been shown to generalize poorly in unconstrained environments, setting back their adoption in applications such as hand gesture control. This problem is often due to limited training
Externí odkaz:
http://arxiv.org/abs/2404.15360
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 false activations in real-world conditions. In this work, a novel myoele
Externí odkaz:
http://arxiv.org/abs/2402.10050
Electromyography (EMG) has been explored as an HCI input modality following a long history of success for prosthesis control. While EMG has the potential to address a range of hands-free interaction needs, it has yet to be widely accepted outside of
Externí odkaz:
http://arxiv.org/abs/2304.00582
Publikováno v:
2020: MEC20
Despite decades of research and development of pattern recognition approaches, the clinical usability of myoelectriccontrolled prostheses is still limited. One of the main issues is the high inter-subject variability that necessitates long and freque
Externí odkaz:
http://arxiv.org/abs/2003.03481
Publikováno v:
2020: MEC20
Recent human computer-interaction (HCI) studies using electromyography (EMG) and inertial measurement units (IMUs) for upper-limb gesture recognition have claimed that inertial measurements alone result in higher classification accuracy than EMG. In
Externí odkaz:
http://arxiv.org/abs/2003.03424
Autor:
Côté-Allard, Ulysse, Gagnon-Turcotte, Gabriel, Phinyomark, Angkoon, Glette, Kyrre, Scheme, Erik, Laviolette, François, Gosselin, Benoit
Publikováno v:
in IEEE Access, vol. 8, pp. 177941-177955, 2020
Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of the si
Externí odkaz:
http://arxiv.org/abs/1912.11037