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. [Display omitted] •A method to classify construction worker activity from sequential kinematics data.•The activity recognition model achieved F1-scores between 0.872 and 0.904.•The framework introduces an efficient and accurate method to do productivity metric estimations for construction workers.•The proposed method enables an automated work sampling method for monitoring painters.•A feature selection method is introduced to prune the feature space and minimize the number of on-body sensors.