Does Exerting Grasps Involve a Finite Set of Muscle Patterns? A Study of Intra- and Intersubject Variability of Forearm sEMG Signals in Seven Grasp Types
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Título
Does Exerting Grasps Involve a Finite Set of Muscle Patterns? A Study of Intra- and Intersubject Variability of Forearm sEMG Signals in Seven Grasp TypesFecha de publicación
2024-03-29Editor
Institute of Electrical and Electronics Engineers Inc.ISSN
1534-4320Cita bibliográfica
N. J. Jarque-Bou, M. Vergara and J. L. Sancho-Bru, "Does Exerting Grasps Involve a Finite Set of Muscle Patterns? A Study of Intra- and Intersubject Variability of Forearm sEMG Signals in Seven Grasp Types," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 1505-1514, 2024, doi: 10.1109/TNSRE.2024.3383156.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/abstract/document/10485525Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Surface Electromyography (sEMG) signals are widely used as input to control robotic devices, prosthetic limbs, exoskeletons, among other devices, and provide information about someone’s intention to perform a particular ... [+]
Surface Electromyography (sEMG) signals are widely used as input to control robotic devices, prosthetic limbs, exoskeletons, among other devices, and provide information about someone’s intention to perform a particular movement. However, the redundant action of 32 muscles in the forearm and hand means that the neuromotor system can select different combinations of muscular activities to perform the same grasp, and these combinations could differ among subjects, and even among the trials done by the same subject. In this work, 22 healthy subjects performed seven representative grasp types (the most commonly used). sEMG signals were recorded from seven representative forearm spots identified in a previous work. Intra- and intersubject variability are presented by using four sEMG characteristics: muscle activity, zero crossing, enhanced wavelength and enhanced mean absolute value. The results confirmed the presence of both intra- and intersubject variability, which evidences the existence of distinct, yet limited, muscle patterns while executing the same grasp. This work underscores the importance of utilizing diverse combinations of sEMG features or characteristics of various natures, such as time-domain or frequency-domain, and it is the first work to observe the effect of considering different muscular patterns during grasps execution. This approach is applicable for fine-tuning the control settings of current sEMG devices. [-]
Publicado en
IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32 (2024)Derechos de acceso
Copyright 2024 The Authors
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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