Cargando…

Latent Factors Limiting the Performance of sEMG-Interfaces

Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human–machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interf...

Descripción completa

Detalles Bibliográficos
Autores principales: Lobov, Sergey, Krilova, Nadia, Kastalskiy, Innokentiy, Kazantsev, Victor, Makarov, Valeri A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948532/
https://www.ncbi.nlm.nih.gov/pubmed/29642410
http://dx.doi.org/10.3390/s18041122
_version_ 1783322570879139840
author Lobov, Sergey
Krilova, Nadia
Kastalskiy, Innokentiy
Kazantsev, Victor
Makarov, Valeri A.
author_facet Lobov, Sergey
Krilova, Nadia
Kastalskiy, Innokentiy
Kazantsev, Victor
Makarov, Valeri A.
author_sort Lobov, Sergey
collection PubMed
description Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human–machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here, we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. Short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures’ fidelity in a dynamic gaming environment. For each individual subject, the approach allows identifying “problematic” gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces.
format Online
Article
Text
id pubmed-5948532
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-59485322018-05-17 Latent Factors Limiting the Performance of sEMG-Interfaces Lobov, Sergey Krilova, Nadia Kastalskiy, Innokentiy Kazantsev, Victor Makarov, Valeri A. Sensors (Basel) Article Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human–machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here, we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. Short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures’ fidelity in a dynamic gaming environment. For each individual subject, the approach allows identifying “problematic” gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces. MDPI 2018-04-06 /pmc/articles/PMC5948532/ /pubmed/29642410 http://dx.doi.org/10.3390/s18041122 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lobov, Sergey
Krilova, Nadia
Kastalskiy, Innokentiy
Kazantsev, Victor
Makarov, Valeri A.
Latent Factors Limiting the Performance of sEMG-Interfaces
title Latent Factors Limiting the Performance of sEMG-Interfaces
title_full Latent Factors Limiting the Performance of sEMG-Interfaces
title_fullStr Latent Factors Limiting the Performance of sEMG-Interfaces
title_full_unstemmed Latent Factors Limiting the Performance of sEMG-Interfaces
title_short Latent Factors Limiting the Performance of sEMG-Interfaces
title_sort latent factors limiting the performance of semg-interfaces
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948532/
https://www.ncbi.nlm.nih.gov/pubmed/29642410
http://dx.doi.org/10.3390/s18041122
work_keys_str_mv AT lobovsergey latentfactorslimitingtheperformanceofsemginterfaces
AT krilovanadia latentfactorslimitingtheperformanceofsemginterfaces
AT kastalskiyinnokentiy latentfactorslimitingtheperformanceofsemginterfaces
AT kazantsevvictor latentfactorslimitingtheperformanceofsemginterfaces
AT makarovvaleria latentfactorslimitingtheperformanceofsemginterfaces