Heart rate modeling and prediction of construction workers based on physical activity using deep learning

Abstract

Construction projects require long working hours where workers are subjected to intensive tasks such as hard manual labor, heavy weightlifting, and compulsive working postures. Among the physiological metrics, Heart Rate (HR) is reported to be a good indicator of physical stress and workload. HR forecasting models have been used in various areas including cardiopathy research, heart attack warning indicator, and early physical fatigue detection. However, there are no reported studies on HR modeling and forecasting in the construction field. Modeling and forecasting the HR of construction workers using construction field data is of paramount importance due to the direct relationship between activity level and HR. The objective of this study is to (1) analyze the effect of physiological factors including breathing rate, acceleration of torso movements, torso posture, and impulse load on the HR of construction workers, and (2) model and forecast one-minute ahead HR for construction workers based on their physical activity using deep learning algorithms. To this end, physiological metrics of five bridge maintenance workers performing several construction activities were collected. According to the Pearson correlation and entropy based mutual information analysis, time-lagged variables including acceleration of torso movements, torso posture, and impulse load have significant effect on the HR data. The results of deep learning models indicate that Long Short-Term Memory Network (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU) have similar predictive performance. However, LSTM had the best overall performance in HR prediction with Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) of 5.4, 7.34, and 5.77%, respectively. These models have the potential to facilitate the mitigation of cardiovascular strain and enable proactive prevention of accidents in the construction industry.