Fatigue and Sluggishness Detection Using Machine Learning: A Haar Algorithmic Approach

Fatigue, Drowsiness, Sluggishness, Exhaustion, and Weariness are important problems in our day-to-day life because our life is becoming so hectic, and tiresome Due to the change in our sleep cycle and Work cycle changes. So this paper introduces a new efficient and practical method,the use of machine learning methods, especially the Haar algorithm, allows the development of complex systems that can accurately identify and evaluate the symptoms of fatigue and human fatigue, thus contributing to the improvement of safety and well-being in various environments. The Haar algorithm is designed for object detection and is suitable for analyzing faces and patterns to identify signs of fatigue and exhaustion in humans. This really helps in many areas such as transportation, health and occupational safety. The Approach will involve capturing live images or videos of people and processing them through a Haar-based algorithm to identify key facial features such as eye-aspect ratio, lowering of eyebrows, yawning, and slow facial movements. We use a machine learning algorithm to identify faces and train the algorithm using a convolutional neural network (CNN), which is used to recognize facial patterns and measure the level of fatigue or exhaustion. This research contributes to solving safety and health issues in situations where fatigue can pose a serious risk, and helps develop interventions to prevent people from reporting their vulnerability.