Machine Learning for Fatigue Estimation and Prediction “An Introduction Study”-Reference-Cited by-同舟云学术

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Machine Learning for Fatigue Estimation and Prediction “An Introduction Study”

Published:2022 Issue: Volume: Page:226-231
ISSN:1680-0737
Container-title:IFMBE Proceedings
language:
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Author:

Aljihmani Lilia,Ursutiu Doru,Cornel Samoila,Qaraqe Khalid

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-93564-1_25

Reference12 articles.

1. Wan, J.-J., Qin, Z., Wang, P.-Y, Sun, Y., Liu, X.: Muscle fatigue: general understanding and treatment. Exp. Mol. Med. 49(10), e384–e384 (2017)

2. Gonzalez, K., Sasangohar, F., Mehta, R.K., Lawley, M., Erraguntla, M.: Measuring fatigue through heart rate variability and activity recognition: a scoping literature review of machine learning techniques. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, pp. 1748–1752. Sage, Los Angeles (2017)

3. Elble, R.J., Higgins, C., Moody, C.J.: Stretch reflex oscillations and essential tremor. J. Neurol. Neurosurg. Psychiatry 50(6), 691–698 (1987)

4. Ebenbichler, G.R., et al.: Bochdansky T:Load-dependence of fatigue related changes in tremor around 10 Hz. Clin. Neurophysiol. 111(1), 106–111 (2000)

5. Elble, R.J.: Tremor Neuro-geriatrics, pp. 311–326. Springer (2017)

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1. A survey of fatigue measures and models;The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology;2023-03-14

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