Computer-aided technologies for posture-based ergonomic risk assessment in construction : a systematic review

Abstract

The limitations of traditional postural ergonomic risk assessment (ERA) methods prompted the construction research community to turn to computer-aided technologies (CATs) for assistance. However, a comprehensive review is lacking to systematically examine the application of CATs for ERA in this context. This study aimed to understand the CATs used for ERA and their implementation trends, evaluate their technological capabilities, and practical applications, identify critical gaps in current research, and provide actionable insights. The systematic literature review (SLR) followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRIaSMA) protocol to retrieve relevant documents from the Scopus and Web of Science (WoS) databases. Based on the researcher’s pre-established inclusion criteria, 85 papers were retrieved and subsequently analyzed through a content analysis approach. The findings revealed eight CATs adopted for ERA in construction wearable sensors, vision-based, machine learning/deep learning, exoskeletons, expert systems, virtual reality, computer-aided design, and digital twins. Further, the analysis uncovered various benefits and limitations of implementing each technology and identified critical gaps in the existing research. The findings emphasize the need for future research to address these gaps, explore broader applications, and integrate innovative solutions to enhance worker occupational health and well-being within the construction domain.