Real-Time Monitoring System Framework for Fall from Roof Accident Prevention

Falls from roof (FFR) is a primary concern for most construction operators given the risk associated with such accidents in the US. FFR accounted for about 30% of total fall cases from 2000 to 2020. Therefore, it is paramount to propose and develop systems to help reduce the occurrence of FFR accidents. The main objectives of this study are to (1) identify and determine the causal relationship between hazards and risk factors associated with FFR; and (2) propose an integrated real-time monitoring framework to mitigate these hazards. To achieve these objectives, a literature review and archival analysis were conducted to investigate the root causes of FFR accidents. This investigation led to the identification of the types of falls, key risk factors, and causal relationships. Next, different digital technologies applications were evaluated based on their ability to mitigate the risk factors, and the most effective sensor technologies for realtime monitoring were considered. The primary sensors for the proposed monitoring framework include computer vision, inertial measurement unit (IMU) sensors, and insole sensors. Finally, a protocol for integrating these sensors is proposed. The present study revealed that the lack of PPE use, loss of balance, unsafe behavior, and manual monitoring are the primary root causes of FFR. The proposed framework has the potential to address hazards associated with FFR. Therefore, the present study contributes to fall prevention management research by developing a framework and protocol for enhancing hazard identification and accident prevention.