Construction Equipment Activity Recognition from IMUs Mounted on Articulated Implements and Supervised Classification

The ability to automatically classify activities performed by various equipment in real-time can assist project managers in reliable decision-making and project control. Such an endeavor requires the identification of individual sequential work-motions (e.g. excavator swinging empty) performed by equipment, which then combine to a specific activity (e.g. excavator loading truck). Towards this end, this paper describes an automated activity recognition framework for construction equipment that uses multiple inertial measurement units (IMU) attached to the equipment’s articulated implements. Initial data were collected using multiple IMUs attached at strategic locations on an excavator. Collected data were then segmented, labelled, and used as inputs in the supervised machine learning classifier. The result demonstrates the ability of the proposed framework to obtain real-time insight into the operation performance of construction equipment using low-cost sensors that are already available on the equipment, but until now only used for pose estimation and automated machine guidance.