Recommending a Machine Learning Model to Detect the Fatigue State for Employees at Namdeb

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Namibia University of Science and Technology


Workplace fatigue is one of the major risk factors across different industries and it negatively impacts productivity and workplace safety. Thus, fatigue detection and monitoring is essential to promote occupational health and safety. The advancements in data collection technologies have made it possible for industries to develop data driven solutions by developing Artificial Intelligence (AI) and Machine Learning (ML) based fatigue monitoring and detection systems. The Advanced Driver Assistance Systems (ADAS) is one of the technologies that has been adopted in various industries to improve safety for drivers. Namdeb implemented the ADAS in recent years and they have identified the need for a system to detect and classify employees’ fatigue state using data from the ADAS. In this study, an ML based fatigue detection system was proposed. Facial behavioural fatigue features were used to detect fatigue. The proposed system deployed some of the commonly used ML classification algorithms and it was evaluated on a simulated dataset, the Yawning Detection Dataset (YawDD), and a real-world dataset, data from the Namdeb ADAS. The results showed that most of the supervised ML classifiers achieved a fatigue prediction accuracy above 90% for both datasets. The Random Forest (RF) based fatigue detection-based model was found to be the best model. The k-Means which is an unsupervised ML classifier exhibited the worst performance. However, the reliability and generalisability of the results based on the real-world dataset can be improved by using a larger dataset. The major challenge to developing behavioural based fatigue detection systems for real world setting like the mining environment is face detection accuracy which is affected by factors such as low image resolution due to poor and variable lighting conditions, face orientation to camera and proximity of face to the camera. The significant contribution of this study is the use of real-world dataset to test the proposed fatigue detection system. Overall, the study contributes to the promotion of the eighth Sustainable Development Goal (SDG) of promoting safe working environments.



Fatigue classification, fatigue detection, fatigue state, machine learning, mining industry, occupational health and safety, video processing


Nakale, S.N. (2024). Recommending a Machine Learning Model to Detect the Fatigue State for Employees at Namdeb [Master’s thesis, Namibia University of Science and Technology].