Machine Learning for Detection of Muscular Activity from Surface EMG Signals.

Journal: Sensors (Basel, Switzerland)
Published Date: Apr 28, 2022

Abstract

BACKGROUND: Muscular-activity timing is useful information that is extractable from surface EMG signals (sEMG). However, a reference method is not available yet. The aim of this study is to investigate the reliability of a novel machine-learning-based approach (DEMANN) in detecting the onset/offset timing of muscle activation from sEMG signals.

Authors

  • Francesco Di Nardo
    Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, 60131, Ancona, Italy. f.dinardo@staff.univpm.it.
  • Antonio Nocera
    Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy.
  • Alessandro Cucchiarelli
    Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy.
  • Sandro Fioretti
    Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, 60131, Ancona, Italy.
  • Christian Morbidoni
    Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, 60131, Ancona, Italy.

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