A novel spatial feature for the identification of motor tasks using high-density electromyography
| Autor: | Joan Francesc Alonso, Hamid Reza Marateb, Mónica Rojas-Martínez, Miguel Angel Mañanas, Mislav Jordanic |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. BIOART - BIOsignal Analysis for Rehabilitation and Therapy |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
030506 rehabilitation
Engineering Electromiografia Speech recognition 0206 medical engineering 02 engineering and technology Electromyography lcsh:Chemical technology Biochemistry Article Analytical Chemistry mean shift 03 medical and health sciences high-density electromyography Robustness (computer science) Pattern recognition medicine lcsh:TP1-1185 Biomechanics Enginyeria biomèdica::Biomecànica [Àrees temàtiques de la UPC] Myoelectric control Mean-shift Electrical and Electronic Engineering prosthetics Instrumentation Prosthetics Mean shift medicine.diagnostic_test business.industry pattern recognition Biomecànica Linear discriminant analysis High-density electromyography 020601 biomedical engineering myoelectric control Atomic and Molecular Physics and Optics 3. Good health Enginyeria biomèdica::Aparells mèdics::Biosensors [Àrees temàtiques de la UPC] Identification (information) Task (computing) Feature (computer vision) Pattern recognition (psychology) 0305 other medical science business |
| Zdroj: | Repositorio U. El Bosque Universidad El Bosque instacron:Universidad El Bosque UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Sensors; Volume 17; Issue 7; Pages: 1597 Sensors (Basel, Switzerland) Recercat. Dipósit de la Recerca de Catalunya instname Sensors, Vol 17, Iss 7, p 1597 (2017) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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