سيدسجاد قريشي

Workforce productivity in the construction industry stands as a pivotal determinant of project success, exerting a profound influence on optimizing time, cost, and quality outcomes, thereby rendering its precise monitoring a cornerstone of construction management research and practice. In recent decades, the adoption of 4th generation industrial technologies (Industry 4.0) has emerged as a focal point for enhancing efficiency in this sector, which has historically suffered from stagnant productivity growth. This study aims to explore the feasibility and development of an innovative system for estimating construction workers’ productivity through a smart wearable device equipped with Inertial Measurement Units (IMUs), comprising accelerometers, gyroscopes, and magnetometers. Designed as an ergonomic wristband, the device continuously captures real-time motion data and employs advanced signal processing algorithms to deliver a comprehensive analysis of worker performance. Focusing on bricklaying as a representative and repetitive task, this research eva‎luates the system’s efficacy against traditional manual observation methods, adopting a mixed-method approach that integrates laboratory calibration with field experiments in real-world settings. Findings reveal an average accuracy of 80% in counting laid bricks, marking a 20% improvement in precision over conventional techniques. However, during extended tasks, an error rate of 67–74% in precisely detecting brick counts was observed, primarily attributable to environmental noise and the complexity of workers’ movement patterns. The system also demonstrated a 90% accuracy in recording active work time compared to human supervisor observations, underscoring its robust capability for continuous monitoring and detection of nonlinear productivity trends. Statistical analyses confirmed a significant correlation between device-generated data and reference observations, affirming the transformative potential of this technology in productivity management. By proposing an empirically grounded mathematical model, this study establishes a scientific framework for predicting and optimizing workforce performance, while highlighting the need for algorithmic refinements to mitigate errors across diverse tasks.

سنسور اينرسي , بهره‌وري , پوشيدني هوشمند , فناوري‌هاي نسل 4 , نظارت مستمر

Inertial sensor , Productivity , Smart wearable , 4th generation technologies , Continuous monitoring

Seyed Sajad Ghoreyshi

Dr. Aliakbar Shirzadi Javid