Preprint Article Version 1 This version is not peer-reviewed

Latent Factors Limiting the Performance of sEMG-Interfaces

Version 1 : Received: 3 April 2018 / Approved: 4 April 2018 / Online: 4 April 2018 (05:07:22 CEST)

A peer-reviewed article of this Preprint also exists.

Lobov, S.; Krilova, N.; Kastalskiy, I.; Kazantsev, V.; Makarov, V.A. Latent Factors Limiting the Performance of sEMG-Interfaces. Sensors 2018, 18, 1122. Lobov, S.; Krilova, N.; Kastalskiy, I.; Kazantsev, V.; Makarov, V.A. Latent Factors Limiting the Performance of sEMG-Interfaces. Sensors 2018, 18, 1122.

Journal reference: Sensors 2018, 18, 1122
DOI: 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 a long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. A 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.

Subject Areas

electromyography; human-computer interface; motor control; pattern classification; artificial neural networks

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