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A novel spatial feature for the identification of motor tasks using high-density electromyography
Jordanic, Mislav; Rojas Martínez, Mónica; Mañanas Villanueva, Miguel Ángel; Alonso López, Joan Francesc; Marateb, Hamid Reza
Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. BIOART - Anàlisi de Biosenyals per a la Rehabilitació i la Teràpia
Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications.
Peer Reviewed
Àrees temàtiques de la UPC::Enginyeria biomèdica::Biomecànica
Àrees temàtiques de la UPC::Enginyeria biomèdica::Aparells mèdics::Biosensors
Electromyography
Biomechanics
high-density electromyography
pattern recognition
myoelectric control
mean shift
prosthetics
Electromiografia
Biomecànica
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/publishedVersion
Artículo
MDPI AG
         

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