Physical Fatigue Detection Using Entropy Analysis of Heart Rate Signals

Farnad Nasirzadeh, Mostafa Mir, Sadiq Hussain, Mohammad Tayarani Darbandy, Abbas Khosravi, Saeid Nahavandi and Brad Aisbett
Additional contact information
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

Sustainability, 2020, vol. 12, issue 7, 1-17

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.

Keywords: fatigue; heart rate; signal processing; entropy; statistical measures; classification algorithms (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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