Worker’s physical fatigue classification using neural networks-Reference-Cited by-同舟云学术

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Worker’s physical fatigue classification using neural networks

Published:2022-07 Issue: Volume:198 Page:116784
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Escobar-Linero Elena^ORCID,Domínguez-Morales Manuel^ORCID,Sevillano José Luis^ORCID

Funder

Federación Española de Enfermedades Raras

European Regional Development Fund

Consejería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference67 articles.

1. Fatigue assessment scales: A comprehensive literature review;Aghdam;Archives of Hygiene Sciences,2019

2. Using machine learning and wearable inertial sensor data for the classification of fractal gait patterns in women and men during load carriage;Ahamed;Procedia Computer Science,2021

3. Deep learning for motor imagery EEG-based classification: A review;Al-Saegh;Biomedical Signal Processing and Control,2021

4. The effect of fatigue and fatigue intensity on exercise tolerance in moderate COPD;Al-shair;Lung,2016

5. Accelerometer-based physical fatigue assessment in 400 meter running event;Atiya;AIP Conference Proceedings,2021

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3. Investigation of Fatigue and Drowsiness of Welders and Goldsmiths Based on Entropies and Complexity Parameters of EOGs: A Statistical Approach;Journal of The Institution of Engineers (India): Series B;2024-07-05

4. Relationship between physical fatigue and mental fatigue based on multimodal measurement under different load levels;Ergonomics;2024-06-24

5. Evaluation of walking activity and gait to identify physical and mental fatigue in neurodegenerative and immune disorders: preliminary insights from the IDEA-FAST feasibility study;Journal of NeuroEngineering and Rehabilitation;2024-06-05

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