Fatigue Detection for the Elderly Using Machine Learning Techniques

Elderly fatigue, a critical issue affecting the health and well-being of the aging population worldwide, presents as a substantial decline in physical and mental activity levels. This widespread condition reduces the quality of life and introduces significant hazards, such as increased accidents and cognitive deterioration. Therefore, this study proposed a model to detect fatigue in the elderly with satisfactory accuracy. In our contribution, we use video and image processing through a video in order to detect the elderly’s face recognition in each frame. The model identifies facial landmarks on the detected face and calculates the Eye Aspect Ratio (EAR), Eye Fixation, Eye Gaze Direction, Mouth Aspect Ratio (MAR), and 3D head pose. Among the various methods evaluated in our study, the Extra Trees algorithm outperformed all others machine learning methods, achieving the highest results with a sensitivity of 98.24%, specificity of 98.35%, and an accuracy of 98.29%.