Detecting excessive load-carrying tasks using a deep learning network with a Gramian Angular Field

Abstract

Manual load carrying without sufficient rest may cause work-related musculoskeletal disorders (WMSDs) and needs to be monitored at construction sites. While previous studies have been able to predict load-carrying modes using multiple wearable inertial measurement unit (IMU) sensors, wearing multiple sensors obtrudes on workers during various construction tasks. In this context, by using a single IMU sensor, this research proposes an automatic detecting technique for excessive carrying -load (DeTECLoad) to predict load-carrying weights and postures simultaneously. DeTECLoad converts the IMU data into image data using a Gramian Angular Field, and then uses a hybrid Convolutional Neural Network-Long Short-Term Memory to classify load-carrying modes from the image data. DeTECLoad provides 92.46% and 96.33% accuracies for the load-carrying weight and posture classifications, respectively. By exploiting DeTECLoad, a construction worker's excessive load-carrying tasks could be managed in situ, helping to prevent construction site WMSDs.