RT Journal Article
T1 Latent Factors Limiting the Performance of sEMG-Interfaces
A1 Lobov, Sergey
A1 Krilova, Nadia
A1 Kastalskiy, Innokentiy
A1 Kazantsev, Victor
A1 Makarov Slizneva, Valeriy
AB 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.
PB MDPI
SN 1424-8220
YR 2018
FD 2018-04-06
LK https://hdl.handle.net/20.500.14352/12436
UL https://hdl.handle.net/20.500.14352/12436
LA eng
NO Ministerio de Economía y Competitividad (MINECO)
NO Ministry of education and science (Russia)
DS Docta Complutense
RD 28 abr 2025