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Physical Fatigue Detection Using Entropy Analysis of Heart Rate Signals

Author

Listed:
  • Farnad Nasirzadeh

    (School of Architecture and Built Environment, Deakin University, Geelong 3220, Australia)

  • Mostafa Mir

    (School of Architecture and Built Environment, Deakin University, Geelong 3220, Australia)

  • Sadiq Hussain

    (System Administrator, Dibrugarh University, Assam 786004, India)

  • Mohammad Tayarani Darbandy

    (School of Architecture, Islamic Azad University Taft, Taft 8991985495, Iran)

  • Abbas Khosravi

    (Institute for Intelligent Systems Research and Innovation (IISRI), Locked Bag 20000, Deakin University, Geelong 3220, Australia)

  • Saeid Nahavandi

    (Institute for Intelligent Systems Research and Innovation (IISRI), Locked Bag 20000, Deakin University, Geelong 3220, Australia)

  • Brad Aisbett

    (Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong 3220, Australia)

Abstract

Physical fatigue is one of the most important and highly prevalent occupational hazards in different industries. This research adopts a new analytical framework to detect workers’ physical fatigue using heart rate measurements. First, desired features are extracted from the heart signals using different entropies and statistical measures. Then, a feature selection method is used to rank features according to their role in classification. Finally, using some of the frequently used classification algorithms, physical fatigue is detected. The experimental results show that the proposed method has excellent performance in recognizing the physical fatigue. The achieved accuracy, sensitivity, and specificity rates for fatigue detection are 90.36%, 82.26%, and 96.2%, respectively. The proposed method provides an efficient tool for accurate and real-time monitoring of physical fatigue and aids to enhance workers’ safety and prevent accidents. It can be useful to develop warning systems against high levels of physical fatigue and design better resting times to improve workers’ safety. This research ultimately aids to improve social sustainability through minimizing work accidents and injuries arising from fatigue.

Suggested Citation

  • Farnad Nasirzadeh & Mostafa Mir & Sadiq Hussain & Mohammad Tayarani Darbandy & Abbas Khosravi & Saeid Nahavandi & Brad Aisbett, 2020. "Physical Fatigue Detection Using Entropy Analysis of Heart Rate Signals," Sustainability, MDPI, vol. 12(7), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2714-:d:338888
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    References listed on IDEAS

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    1. Zhihao Duan & Jinliang Xu & Han Ru & Menghui Li, 2019. "Classification of Driving Fatigue in High-Altitude Areas," Sustainability, MDPI, vol. 11(3), pages 1-10, February.
    2. Margaret Chan, 2011. "Fatigue: the most critical accident risk in oil and gas construction," Construction Management and Economics, Taylor & Francis Journals, vol. 29(4), pages 341-353.
    3. Umberto C. Gatti & Giovanni C. Migliaccio & Susan M. Bogus & Suzanne Schneider, 2014. "An exploratory study of the relationship between construction workforce physical strain and task level productivity," Construction Management and Economics, Taylor & Francis Journals, vol. 32(6), pages 548-564, June.
    4. Jaewoon Lee & Dongho Kim & Han-Young Ryoo & Byeong-Seok Shin, 2016. "Sustainable Wearables: Wearable Technology for Enhancing the Quality of Human Life," Sustainability, MDPI, vol. 8(5), pages 1-16, May.
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    Cited by:

    1. Gjulio Ashta & Serena Finco & Daria Battini & Alessandro Persona, 2023. "Passive Exoskeletons to Enhance Workforce Sustainability: Literature Review and Future Research Agenda," Sustainability, MDPI, vol. 15(9), pages 1-26, April.
    2. Valeriia Demareva & Irina Zayceva & Valeriia Viakhireva & Marina Zhukova & Ekaterina Selezneva & Ekaterina Tikhomirova, 2023. "Home-Based Dynamics of Sleepiness-Related Conditions Starting at Biological Evening and Later (Beyond Working)," IJERPH, MDPI, vol. 20(17), pages 1-14, August.

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