Work estimation of construction workers for productivity monitoring using kinematic data and deep learning

In this study, a novel method for work estimation is presented. The aim is to build an accurate and reliable work classification algorithm that can help monitor construction sites without unnecessarily constraining the workers or installing heavy sensing infrastructure. The method utilizes deep learning algorithms to classify multivariate time-series data collected from five inertial measurement units mounted on the worker. Three models are developed, differing in window sizes from 3 to 7 s. The best performing model achieves an accuracy of 90% and an F1 score of 0.876. The model is analyzed and pruned using expected gradients for feature selection. The process reduces the input space by 60%, equivalent to 3 sensors. This is an initial step towards a general model that can classify productivity measures for workers on construction sites, which will provide valuable input for monitoring construction site activities and future analyses such as forecasting productivity.

Automated summary

This study presents a novel method for work estimation, aiming to develop an accurate and reliable work classification algorithm for monitoring construction sites. Deep learning algorithms are utilized to classify multivariate time-series data collected from inertial measurement units mounted on the worker. Three models with different window sizes are developed, and the best performing model achieves 90% accuracy and an F1 score of 0.876. The model is analyzed and pruned using expected gradients for feature selection, reducing the input space by 60% equivalent to 3 sensors. This is an initial step towards a general model for classifying productivity measures for workers on construction sites, which can provide valuable input for monitoring activities and forecasting productivity.