Developing Prediction Models for Monitoring Workers' Fatigue in Hot Conditions

Fatigue is a common problem in the construction industry due to the labor-intensive, repetitive, and physically demanding nature of the activities. Most construction projects are carried out in the summer, when the temperature typically ranges from 90 degrees F to 110 degrees F, exposing workers to the danger of heightened fatigue and heat exhaustion. Although studies have shown that fatigue and heat have a detrimental effect on the safety of workers, few studies have assessed and predicted workers' fatigue levels in hot conditions using physiological metrics. Thus, the need for studies focused on developing predictive solutions in this domain. In this study, wearable sensing devices such as electromyography, heart rate, and heart rate variability sensors were used to track the fatigue levels of eight individuals in real time as they performed repetitive tasks in hot conditions (95 degrees F). In addition, participants observed fatigue was captured during the repetitive task using a step-observational approach. Seventeen features were extracted from the physiological data to train machine learning classifiers such as random forest classifiers, support vector machines, and KNN. The model developed from this study has the capacity to predict workers' perception of fatigue in hot conditions and could be further integrated into wearable devices that will be used by construction workers and professionals to monitor their fatigue levels in real time.