Artificial neural network-based slip-trip classifier using smart sensor for construction workplace

This paper presents a smart artificial neural network (ANN)-based slip-trip classification method, which integrates a smart sensor and an ANN. It was trained to identify the slip and trip events that occur while a worker walks in a workplace. It encourages preventive and collective actions to reduce construction accidents by identifying the type of near miss, i.e., slip or trip, and the exact time that it occurs. The variation in the energy released by a worker is measured using a triaxial accelerometer embedded in a smart phone. This study is of value to researchers because the method measures a near miss quantitatively using acceleration. It is also of relevance to practitioners because it provides a computerized tool that records each and every moment of a near-miss event. A test was performed by collecting the three-axis acceleration streams generated by workers wearing a smart phone running the classifier as they walked around a simulated construction jobsite. It identified the type of near miss and the exact time of its occurrence. The test case verified the usability and validity of the computational methods.