Smart work package learning for decentralized fatigue monitoring through facial images

Monitoring the fatigue of construction equipment operators (CEOs) is critical for preventing accidents and ensuring precision construction occupational health and safety (COHS). However, there exists a theoretical dilemma between centralized technical efficiency and decentralized data privacy. Thus, this study introduces smart work package learning (SWPL), a decentralized deep learning approach to monitor CEOs' fatigue without privacy exposure risks. To illustrate the feasibility of SWPL as the fatigue classifier, this study implements fatigue monitoring through noninvasive facial images, and SWPL merges the updated parameters of the model from each smart work package (SWP). These updates are then validated by SWPs in the blockchain network and stored on the blockchain. More than 356 videos were derived from 124 operators. The results present that SWPL on decentralized SWP networks outperforms the deep learning model on individual SWP. The computational novelty is SWPL's dynamic parameter aggregation mechanism to avoid parameter exposure in centralized or fixed aggregators. The proposed SWPL will open up advanced developments in precision COHS.

Automated summary

This study introduces a decentralized deep learning approach (SWPL) to monitor the fatigue of construction equipment operators in order to ensure occupational health and safety. The results demonstrate that SWPL outperforms individual work packages in a decentralized network.