Evaluating the Feasibility of Personalized Health Status Feedback to Enhance Worker Safety and Well-Being at Construction Jobsites

Advancements in wearable sensor technology and AI provide an excellent opportunity to monitor the health status of construction workers on-site. However, there is still a lack of efficient means of promptly communicating this information to workers without violating their privacy. This study evaluates the feasibility of providing personal feedback to construction workers regarding their health status while carrying out routine tasks. The proposed mechanism employs machine learning models and a decision tree to provide workers with timely private feedback and corresponding recommendations or risk mitigation strategies. As such, an experiment was conducted to evaluate the performance of the proposed feedback system as well as users' perception of its usability. The findings revealed that the proposed feedback system could provide the workers with accurate and effective feedback regarding their health status, indicating the great potential to enhance worker safety and well-being at construction jobsites.