Doctor of Philosophy (PhD)


Engineering Science

Document Type



About 40% of the US construction workforce experiences high-level fatigue, which leads to poor judgment, increased risk of injuries, a decrease in productivity, and a lower quality of work. Excessive fatigue from working in unpleasant working conditions, long working hours, or heavy workloads can aggravate fatigue's adverse effects, leading to work-related musculoskeletal disorders (WMSDs) and productivity loss. Therefore, it is essential to monitor fatigue to reduce the adverse effects and preventing long-term health problems. However, since fatigue demonstrates itself in several complex processes, there is no single standard measurement method for fatigue detection. This research aims to develop a system for continuous workers' fatigue monitoring by predicting aerobic fatigue threshold (AFT) according to forearm muscle activity and motion data. The forearm muscle activity and motion data were acquired using a low-cost, non-invasive, wearable sensor. The proposed fatigue monitoring system consists of multiple measurable frameworks with five objectives: (1) assess the data quality and reliability of forearm motion and muscle activity data, (2) develop and validate the construction workers' activity recognition framework, (3) estimate construction activity-specific maximum aerobic capacity, (4) develop and validate continuous oxygen uptake prediction framework, and (5) develop fatigue level classifier using AFT features and validate the proposed fatigue monitoring system. The proposed system was evaluated on the participants performing fourteen scaffold building activities. The results show that the AFT features have achieved a higher accuracy of 92.31% in assessing the workers' fatigue level compared to heart rate (51.28%) and percentage heart rate reserve (50.43%) features. Moreover, the overall performance of the proposed fatigue monitoring system on unseen data using average 2-min AFT features was 76.74%. The study validates the feasibility of using forearm muscle activity and motion data to monitor the workers' fatigue level continuously. The performance of the proposed system shows some promising potentials that it can be applied on the construction field to help assess worker's physiological status, evaluate the physical workload of the activity, quantify the direct impacts of the fatigue level on the accidents, and enhance the workers' safety, health, and productivity through early detection of risk.

Committee Chair

Wang, Chao



Available for download on Saturday, May 11, 2024