Automating the Estimation of Productivity Metrics for Construction Workers Using Deep Learning and Kinematics

Abstract

In this study, a novel method for direct work estimation is used to classify whether a painteris performingdirect workor not.The aim is to build an accurate and reliable work classification algorithmthatcanhelp monitorconstruction sites.The method utilizes adeep learning algorithmusing convolutional and long short-term memory layersto classify multivariate time-series data collected from five inertial measurement units (IMUs)mounted on the workers’arms, torso,and legs.Three models are developed, differing in window sizes from 3 seconds to 7 seconds.Thebest performingmodel achieves an accuracy of 90%and anF1-score of 87.6%. This is thefirst step towards a general model that canclassify productivity measures for workers on construction sites, which will be a valuableinput formonitoringconstruction sitesandfuture analyses.