An analytical framework using entropy analysis of heart rate signals detected physical fatigue with 90.36% accuracy, 82.26% sensitivity, and 96.2% specificity.
Does an analytical framework using entropy analysis of heart rate signals accurately detect physical fatigue in workers?
An entropy-based analytical framework using heart rate signals can accurately detect physical fatigue, potentially improving worker safety.
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.
Nasirzadeh et al. (Mon,) conducted a other in Physical fatigue. Entropy analysis of heart rate signals was evaluated on Fatigue detection accuracy. An analytical framework using entropy analysis of heart rate signals detected physical fatigue with 90.36% accuracy, 82.26% sensitivity, and 96.2% specificity.