Improvements on Integrated Health and Safety Management System based on Wi-Pose to increase Productivity

Abstract

During the health crisis, construction companies are under tremendous pressure to adopt innovative techniques for employee management to meet the health and safety demands and reinstate productivity, starting from managing attendance and monitoring performance. The construction industry can implement more equipment's and robots to address worker safety while maintaining a safe distance, thus reducing the labor force. This intern results in man-machine interaction conflicts and accidents due to the failure of machines or components that needs addressing. Most workplace accidents in South Korea occur in the construction industry, and 60% of them are due to falls from heights. The aging population in South Korea makes the construction industry more prone to mental fatigue, resulting in such accidents. Thus, this study aims to integrate systems of Health and Safety with Performance using pose estimation techniques. Using deep learning techniques, IIOT (Industrial Internet of Things), ICT with wireless access points, and wearable devices such as smart bands, shoes, and helmets aids to monitor workers' Performance, Health, and safety in indoor conditions or after structural frame completion. Combining previous warning alert systems with pose estimation and monitoring will alert workers on unsafe and uncomfortable poses and improve productivity significantly. A future version of this system could use LoRa to extend to outdoor environments.