The Effect of Signal-to-Noise Ratio on Muscle Synergy Extraction

Marco Ghislieri1, Valentina Agostini1, Marco Knaflitz

  • 1Politecnico di Torino

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Poster Session

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10:00 - 17:00 | Tue 30 Oct | Foyer | B1P-E

Rehabilitation & Assistive Technologies

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Abstract

The modular organization of the central nervous system (CNS) during motor tasks was widely assessed by means of muscle synergies. The aim of this work was to assess the impact of the Signal-to-Noise Ratio (SNR) of surface electromyographic signals (sEMG) on muscle synergy extraction algorithms. To evaluate the effect of the SNR, the similarity between the weights vectors and activation coefficients extracted from real sEMG signals and from simulated sEMG signals at different values of SNR was computed. Results reveal that muscle synergy extraction is strongly dependent upon the quality of the sEMG signals simulated.

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