Latent Factors Limiting the Performance of sEMG-Interfaces

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Lobov, Sergey and Krilova, Nadia and Kastalskiy, Innokentiy and Kazantsev, Victor and Makarov, Valeri A. (2018) Latent Factors Limiting the Performance of sEMG-Interfaces. Sensors, 18 (4). p. 1122. ISSN 1424-8220

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Official URL: https://doi.org/10.3390/s18041122




Abstract

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.


Item Type:Article
Uncontrolled Keywords:Electromyography; Human–computer interface; Motor control; Pattern classification; Artificial neural networks
Subjects:Sciences > Mathematics > Cybernetics
Sciences > Mathematics > Mathematical statistics
Medical sciences > Medicine > Neurosciences
ID Code:63197
Deposited On:30 Nov 2020 17:41
Last Modified:01 Dec 2020 08:14

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