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A New Method for Detecting the Fatigue Using Automated Deep Learning Techniques for Medical Imaging Applications

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Abstract

In the human race, Fatigue may contribute to a decline in efficiency. Fatigue is a risk factor for health and a component of quality degradation. The effects of Fatigue include sleep disorders, depression, and worry, all of which can lead to life-threatening issues. This project uses machine learning and deep learning techniques to identify a person’s degree of Fatigue and its effects. The developed deep learning network can accurately distinguish between normal and exhausted states. This detection device uses physiological characteristics to guarantee high detection rates and accuracy. The project aims to recognize fatigue levels by examining the features extracted from the batch of images and classifying them into their respective class labels, such as Alert, Non-Vigilant, and Fatigued. To implement the detection procedures using deep structured learning that will yield very accurate image recognition and classification results, a large-scale image dataset is transported through efficient algorithm strategies and is processed to transform the data by labelling the patterns, tracking the correlations, and producing supreme results. The images will be diagnosed by employing the pre-trained models of the Convolutional Neural Networks (CNN), convolving through the hidden layers, applying the filters, and sharing the weights. Alex Net, Resnet50, and MobilenetV2 are the potential classifiers that will expand, filter, train, compress, and test through the neurons of the subjects. The layers and the non-linear functionalities are designed in the wake of the structured embedding of the model to deliver efficient metrics. The proposal offers the best accuracy for the established MobilenetV2 model with 99.8% accuracy and validates it with high-performance results.

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Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Funding

This work was supported by the Qatar National Research Fund under the grant number MME03-1226- 210042. The statements made herein are solely the responsibility of the authors.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

    Naveen Sundar Gnanadesigan, Grace Angela Abraham Lincoln & Narmadha Dhanasegar

  2. Department of Electrical and Electronics Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu, India

    Suresh Muthusamy

  3. School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India

    Deeba Kannan

  4. Department of Computer Science and Engineering, National Institute of Technology Puducherry, Puducherry, India

    Surendiran Balasubramanian

  5. Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000, Belgrade, Serbia

    Nebojsa Bacanin

  6. Department of Mathematics, Saveetha School of Engineering (Deemed to be University), SIMATS Thandalam, Chennai, 602105, Tamil Nadu, India

    Nebojsa Bacanin

  7. MEU Research Unit, Middle East University, Amman, Jordan

    Nebojsa Bacanin

  8. Centre for Advanced Materials, Qatar University, 2713, Doha, Qatar

    Kishor Kumar Sadasivuni

  9. Department of Mechanical and Industrial Engineering, Centre for Advanced Materials, Qatar University, 2713, Doha, Qatar

    Kishor Kumar Sadasivuni

Authors
  1. Naveen Sundar Gnanadesigan
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  2. Grace Angela Abraham Lincoln
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  3. Narmadha Dhanasegar
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  4. Suresh Muthusamy
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  5. Deeba Kannan
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  6. Surendiran Balasubramanian
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  7. Nebojsa Bacanin
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  8. Kishor Kumar Sadasivuni
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Contributions

All authors contributed equally for the preparation of the manuscript. Naveen Sundar Gnanadesigan, Grace Angela Abraham Lincoln—Study conception and design. Narmadha Dhanasegar,Suresh Muthusamy—Data collection and analysis. Deeba Kannan, Surendiran Balasubramanian—Interpretation of results and manuscript preparation. Nebojsa Bacanin, Kishor Kumar Sadasivuni—Overall supervision of the work.

Corresponding author

Correspondence to Suresh Muthusamy.

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Gnanadesigan, N.S., Lincoln, G.A.A., Dhanasegar, N. et al. A New Method for Detecting the Fatigue Using Automated Deep Learning Techniques for Medical Imaging Applications. Wireless Pers Commun 135, 1009–1034 (2024). https://doi.org/10.1007/s11277-024-11102-6

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