A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables

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Date
2024-04-27
Authors
Yasar, Merve Nur
Sica, Marco
O'Flynn, Brendan
Tedesco, Salvatore
Menolotto, Matteo
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Springer
Published Version
https://doi.org/10.1038/s41597-024-03254-8
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Abstract
Wearable sensors have recently been extensively used in sports science, physical rehabilitation, and industry providing feedback on physical fatigue. Information obtained from wearable sensors can be analyzed by predictive analytics methods, such as machine learning algorithms, to determine fatigue during shoulder joint movements, which have complex biomechanics. The presented dataset aims to provide data collected via wearable sensors during a fatigue protocol involving dynamic shoulder internal rotation (IR) and external rotation (ER) movements. Thirty-four healthy subjects performed shoulder IR and ER movements with different percentages of maximal voluntary isometric contraction (MVIC) force until they reached the maximal exertion. The dataset includes demographic information, anthropometric measurements, MVIC force measurements, and digital data captured via surface electromyography, inertial measurement unit, and photoplethysmography, as well as self-reported assessments using the Borg rating scale of perceived exertion and the Karolinska sleepiness scale. This comprehensive dataset provides valuable insights into physical fatigue assessment, allowing the development of fatigue detection/prediction algorithms and the study of human biomechanical characteristics during shoulder movements within a fatigue protocol.
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Keywords
Biomedical engineering , Research data , Wearable sensors , Physical fatigue assessment , Fatigue
Citation
Yasar, M.N., Sica, M., O’Flynn, B., Tedesco, S. and Menolotto, M. (2024) ‘A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables’, Scientific Data, 11(1), p. 433. Available at: https://doi.org/10.1038/s41597-024-03254-8
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https://hdl.handle.net/10468/16000
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Tyndall National Institute - Journal Articles
Insight Centre for Data Analytics - Journal Articles
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© The Authors 2024. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.