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Fatigue Detection Using Artificial Intelligence Framework

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Abstract

Technological advances in healthcare have saved innumerable patients and are continuously improving our quality of life. Fatigue among health indicators of individuals has become significant due to its association with cognitive performance and health outcomes and, is one of the major factors contributing to the degradation of performance in daily life. This review serves as a source of studies which helped in better understanding of fatigue and also gave significant detection methods and systematic approaches to figure out the impacts and causes of fatigue. Artificial intelligence was turned out to be one of the essential tactics to detect or monitor fatigue. Artificial neural network, wavelet transform, data analysis of mouse interaction and keyboard patterns, image analysis, kernel learning algorithms, relation of fatigue and anxiety, and heart rate data examination studies were used in this paper to precisely assess the source, factors and features which influenced the recognition of fatigue.

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Acknowledgements

The authors are grateful to the Department of Chemical Engineering, School of Technology, Pandit Deendayal Petroleum University and Indus University for permission to publish this research.

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  1. Department of Computer Engineering, Indus University, Ahmedabad, Gujarat, India

    Vidhi Parekh & Darshan Shah

  2. Department of Chemical Engineering School of Technology, Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, India

    Manan Shah

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All the authors make substantial contribution in this manuscript. VP, DS and MS participated in drafting the manuscript. VP and DS wrote the main manuscript, and all the authors discussed the results and implication on the manuscript at all stages.

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Parekh, V., Shah, D. & Shah, M. Fatigue Detection Using Artificial Intelligence Framework. Augment Hum Res 5, 5 (2020). https://doi.org/10.1007/s41133-019-0023-4

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