High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans

This paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four different isometric tasks at different effort levels associated...

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Autores:
Rojas-Martínez, Mónica
Serna Higuita, Leidy Yanet
Jordanic, Mislav
Marateb, Hamid Reza
Merletti, Roberto
Mañanas, Miguel Angel
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Universidad El Bosque
Repositorio:
Repositorio U. El Bosque
Idioma:
eng
OAI Identifier:
oai:repositorio.unbosque.edu.co:20.500.12495/5181
Acceso en línea:
http://hdl.handle.net/20.500.12495/5181
https://doi.org/10.6084/m9.figshare.12808307
Palabra clave:
Electromiografía
Fatiga muscular
Contracción isométrica
Rights
openAccess
License
Attribution 4.0 International
Description
Summary:This paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four different isometric tasks at different effort levels associated with movements of the forearm. Three 2-D electrode arrays were used for recording the myoelectric activity from five upper limb muscles: biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres. Technical validation comprised a signals quality assessment from outlier detection algorithms based on supervised and non-supervised classification methods. About 6% of the total number of signals were identified as “bad” channels demonstrating the high quality of the recordings. In addition, spatial and intensity features of HD-sEMG maps for identification of effort type and level, have been formulated in the framework of this database, demonstrating better performance than the traditional time-domain features. The presented database can be used for pattern recognition and MUAP identification among other uses.