Analysis of Surface Electromyography for On-Off Control

Seng, C. Bun; Sia, Charles; Wong, Farrah; Chin, Renee Ka Yin; Dargham, Jamal Ahmad; Siang, Yang Soo

doi:10.4028/www.scientific.net/amr.701.435

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…Most methods of extraction of the representative features from sEMG signals are based either on amplitude characteristics and autoregressive models or on the time-frequency analysis and spatiotemporal features [ 9 , 16 , 17 ]. The pattern classification is usually achieved by linear discriminant analysis (LDA), support vector machines, Bayesian statistics, and artificial neural networks (ANN) [ 16 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Latent Factors Limiting the Performance of sEMG-Interfaces

Lobov

Krilova

Kastalskiy

et al. 2018

Sensors

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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.

show abstract

Section: Introductionmentioning

confidence: 99%

Latent Factors Limiting the Performance of sEMG-Interfaces

Lobov

Krilova

Kastalskiy

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Most methods of extraction of the representative features from sEMG signals are based either on amplitude characteristics and autoregressive models or on the time-frequency analysis and spatiotemporal features [9,16,17]. The pattern classification is usually achieved by linear discriminant analysis (LDA), support vector machines, Bayesian statistics, and artificial neural networks (ANN) [16,[18][19][20][21][22][23][24].…”

Section: Of 20mentioning

confidence: 99%

Latent Factors Limiting the Performance of sEMG-Interfaces

Lobov¹,

Krilova²,

Kastalskiy³

et al. 2018

Preprint

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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.

show abstract