Cargando…
Artificial Proprioceptive Feedback for Myoelectric Control
The typical control of myoelectric interfaces, whether in laboratory settings or real-life prosthetic applications, largely relies on visual feedback because proprioceptive signals from the controlling muscles are either not available or very noisy. We conducted a set of experiments to test whether...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610977/ https://www.ncbi.nlm.nih.gov/pubmed/25216484 http://dx.doi.org/10.1109/TNSRE.2014.2355856 |
_version_ | 1783605247974834176 |
---|---|
author | Pistohl, Tobias Joshi, Deepak Ganesh, Gowrishankar Jackson, Andrew Nazarpour, Kianoush |
author_facet | Pistohl, Tobias Joshi, Deepak Ganesh, Gowrishankar Jackson, Andrew Nazarpour, Kianoush |
author_sort | Pistohl, Tobias |
collection | PubMed |
description | The typical control of myoelectric interfaces, whether in laboratory settings or real-life prosthetic applications, largely relies on visual feedback because proprioceptive signals from the controlling muscles are either not available or very noisy. We conducted a set of experiments to test whether artificial proprioceptive feedback, delivered non-invasively to another limb, can improve control of a two-dimensional myoelectrically-controlled computer interface. In these experiments, participants’ were required to reach a target with a visual cursor that was controlled by electromyogram signals recorded from muscles of the left hand, while they were provided with an additional proprioceptive feedback on their right arm by moving it with a robotic manipulandum. Provision of additional artificial proprioceptive feedback improved the angular accuracy of their movements when compared to using visual feedback alone but did not increase the overall accuracy quantified with the average distance between the cursor and the target. The advantages conferred by proprioception were present only when the proprioceptive feedback had similar orientation to the visual feedback in the task space and not when it was mirrored, demonstrating the importance of congruency in feedback modalities for multi-sensory integration. Our results reveal the ability of the human motor system to learn new inter-limb sensory-motor associations; the motor system can utilize task-related sensory feedback, even when it is available on a limb distinct from the one being actuated. In addition, the proposed task structure provides a flexible test paradigm by which the effectiveness of various sensory feedback and multi-sensory integration for myoelectric prosthesis control can be evaluated. |
format | Online Article Text |
id | pubmed-7610977 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
record_format | MEDLINE/PubMed |
spelling | pubmed-76109772021-06-14 Artificial Proprioceptive Feedback for Myoelectric Control Pistohl, Tobias Joshi, Deepak Ganesh, Gowrishankar Jackson, Andrew Nazarpour, Kianoush IEEE Trans Neural Syst Rehabil Eng Article The typical control of myoelectric interfaces, whether in laboratory settings or real-life prosthetic applications, largely relies on visual feedback because proprioceptive signals from the controlling muscles are either not available or very noisy. We conducted a set of experiments to test whether artificial proprioceptive feedback, delivered non-invasively to another limb, can improve control of a two-dimensional myoelectrically-controlled computer interface. In these experiments, participants’ were required to reach a target with a visual cursor that was controlled by electromyogram signals recorded from muscles of the left hand, while they were provided with an additional proprioceptive feedback on their right arm by moving it with a robotic manipulandum. Provision of additional artificial proprioceptive feedback improved the angular accuracy of their movements when compared to using visual feedback alone but did not increase the overall accuracy quantified with the average distance between the cursor and the target. The advantages conferred by proprioception were present only when the proprioceptive feedback had similar orientation to the visual feedback in the task space and not when it was mirrored, demonstrating the importance of congruency in feedback modalities for multi-sensory integration. Our results reveal the ability of the human motor system to learn new inter-limb sensory-motor associations; the motor system can utilize task-related sensory feedback, even when it is available on a limb distinct from the one being actuated. In addition, the proposed task structure provides a flexible test paradigm by which the effectiveness of various sensory feedback and multi-sensory integration for myoelectric prosthesis control can be evaluated. 2015-05-01 2014-09-09 /pmc/articles/PMC7610977/ /pubmed/25216484 http://dx.doi.org/10.1109/TNSRE.2014.2355856 Text en https://creativecommons.org/licenses/by/3.0/This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see https://creativecommons.org/licenses/by/3.0/. |
spellingShingle | Article Pistohl, Tobias Joshi, Deepak Ganesh, Gowrishankar Jackson, Andrew Nazarpour, Kianoush Artificial Proprioceptive Feedback for Myoelectric Control |
title | Artificial Proprioceptive Feedback for Myoelectric Control |
title_full | Artificial Proprioceptive Feedback for Myoelectric Control |
title_fullStr | Artificial Proprioceptive Feedback for Myoelectric Control |
title_full_unstemmed | Artificial Proprioceptive Feedback for Myoelectric Control |
title_short | Artificial Proprioceptive Feedback for Myoelectric Control |
title_sort | artificial proprioceptive feedback for myoelectric control |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610977/ https://www.ncbi.nlm.nih.gov/pubmed/25216484 http://dx.doi.org/10.1109/TNSRE.2014.2355856 |
work_keys_str_mv | AT pistohltobias artificialproprioceptivefeedbackformyoelectriccontrol AT joshideepak artificialproprioceptivefeedbackformyoelectriccontrol AT ganeshgowrishankar artificialproprioceptivefeedbackformyoelectriccontrol AT jacksonandrew artificialproprioceptivefeedbackformyoelectriccontrol AT nazarpourkianoush artificialproprioceptivefeedbackformyoelectriccontrol |