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Towards Soft Wearable Strain Sensors for Muscle Activity Monitoring

The force-generating capacity of skeletal muscle is an important metric in the evaluation and diagnosis of musculoskeletal health. Measuring changes in muscle force exertion is essential for tracking the progress of athletes during training, for evaluating patients’ recovery after muscle injury, and...

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Detalles Bibliográficos
Autores principales: Alvarez, Jonathan T., Gerez, Lucas F., Araromi, Oluwaseun A., Hunter, Jessica G., Choe, Dabin K., Payne, Christopher J., Wood, Robert J., Walsh, Conor J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9421605/
https://www.ncbi.nlm.nih.gov/pubmed/35925858
http://dx.doi.org/10.1109/TNSRE.2022.3196501
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author Alvarez, Jonathan T.
Gerez, Lucas F.
Araromi, Oluwaseun A.
Hunter, Jessica G.
Choe, Dabin K.
Payne, Christopher J.
Wood, Robert J.
Walsh, Conor J.
author_facet Alvarez, Jonathan T.
Gerez, Lucas F.
Araromi, Oluwaseun A.
Hunter, Jessica G.
Choe, Dabin K.
Payne, Christopher J.
Wood, Robert J.
Walsh, Conor J.
author_sort Alvarez, Jonathan T.
collection PubMed
description The force-generating capacity of skeletal muscle is an important metric in the evaluation and diagnosis of musculoskeletal health. Measuring changes in muscle force exertion is essential for tracking the progress of athletes during training, for evaluating patients’ recovery after muscle injury, and also for assisting the diagnosis of conditions such as muscular dystrophy, multiple sclerosis, or Parkinson’s disease. Traditional hardware for strength evaluation requires technical training for operation, generates discrete time points for muscle assessment, and is implemented in controlled settings. The ability to continuously monitor muscle force without restricting the range of motion or adapting the exercise protocol to suit specific hardware would allow for a richer dataset that can help unlock critical features of muscle health and strength evaluation. In this paper, we employ wearable, ultra-sensitive soft strain sensors for tracking changes in muscle deformation during contractions. We demonstrate the sensors’ sensitivity to isometric contractions, as well as the sensors’ capacity to track changes in peak torque over the course of an isokinetic fatiguing protocol for the knee extensors. The wearable soft system was able to efficiently estimate peak joint torque reduction caused by muscle fatigue (mean NRMSE = 0.15±0.03).
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spelling pubmed-94216052022-08-29 Towards Soft Wearable Strain Sensors for Muscle Activity Monitoring Alvarez, Jonathan T. Gerez, Lucas F. Araromi, Oluwaseun A. Hunter, Jessica G. Choe, Dabin K. Payne, Christopher J. Wood, Robert J. Walsh, Conor J. IEEE Trans Neural Syst Rehabil Eng Article The force-generating capacity of skeletal muscle is an important metric in the evaluation and diagnosis of musculoskeletal health. Measuring changes in muscle force exertion is essential for tracking the progress of athletes during training, for evaluating patients’ recovery after muscle injury, and also for assisting the diagnosis of conditions such as muscular dystrophy, multiple sclerosis, or Parkinson’s disease. Traditional hardware for strength evaluation requires technical training for operation, generates discrete time points for muscle assessment, and is implemented in controlled settings. The ability to continuously monitor muscle force without restricting the range of motion or adapting the exercise protocol to suit specific hardware would allow for a richer dataset that can help unlock critical features of muscle health and strength evaluation. In this paper, we employ wearable, ultra-sensitive soft strain sensors for tracking changes in muscle deformation during contractions. We demonstrate the sensors’ sensitivity to isometric contractions, as well as the sensors’ capacity to track changes in peak torque over the course of an isokinetic fatiguing protocol for the knee extensors. The wearable soft system was able to efficiently estimate peak joint torque reduction caused by muscle fatigue (mean NRMSE = 0.15±0.03). 2022 2022-08-11 /pmc/articles/PMC9421605/ /pubmed/35925858 http://dx.doi.org/10.1109/TNSRE.2022.3196501 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Alvarez, Jonathan T.
Gerez, Lucas F.
Araromi, Oluwaseun A.
Hunter, Jessica G.
Choe, Dabin K.
Payne, Christopher J.
Wood, Robert J.
Walsh, Conor J.
Towards Soft Wearable Strain Sensors for Muscle Activity Monitoring
title Towards Soft Wearable Strain Sensors for Muscle Activity Monitoring
title_full Towards Soft Wearable Strain Sensors for Muscle Activity Monitoring
title_fullStr Towards Soft Wearable Strain Sensors for Muscle Activity Monitoring
title_full_unstemmed Towards Soft Wearable Strain Sensors for Muscle Activity Monitoring
title_short Towards Soft Wearable Strain Sensors for Muscle Activity Monitoring
title_sort towards soft wearable strain sensors for muscle activity monitoring
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9421605/
https://www.ncbi.nlm.nih.gov/pubmed/35925858
http://dx.doi.org/10.1109/TNSRE.2022.3196501
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