Nonintrusive Fatigue Detection Based on Multidomain Features of Sitting Pressure and Machine Learning

Abstract

Fatigue significantly reduces productivity and cognitive performance by impairing the concentration and information-processing abilities of individuals. Existing fatigue detection methods often rely on invasive or privacy-disrupting techniques, limiting their applicability in real-world settings. To address these challenges, we propose a nonintrusive fatigue detection method using multidomain features of sitting pressure and machine learning. First, we design a wireless and flexible mat with an embedded pressure sensor array to acquire sitting pressure nonintrusively. Then, we extract multidomain features (time, frequency, and time-frequency domain) and apply feature selection techniques to find an optimal feature subset. We then detect fatigue using well-known machine learning methods based on full domain features and the optimal feature subset, respectively. Experimental results show that utilizing feature selection can improve the performance of fatigue detection and reduce computational complexity. Compared to single classifiers, ensemble learning models require more computational resources but achieve better performance in fatigue detection, with accuracies above 92%. Finally, we use the bootstrap method to evaluate the stability of each model, and the results show that random forest (RF) with the subset excels in terms of stability.