Smartphone-based construction workers activity recognition and classification
Understanding the state, behavior, and surrounding context of construction workers is essential to effective project management and control. Exploiting the integrated sensors of ubiquitous mobile phones offers an unprecedented opportunity for an automated approach to workers’ activity recognition. In addition, machine learning (ML) methodologies provide the complementary computational part of the process. In this paper, smartphones are used in an unobtrusive way to capture body movements by collecting data using embedded accelerometer and gyroscope sensors. Construction activities of various types have been simulated and collected data are used to train five different types of ML algorithms. Activity recognition accuracy analysis has been performed for all the different categories of activities and ML classifiers in user-dependent and -independent ways. Results indicate that neural networks outperform other classifiers by offering an accuracy ranging from 87% to 97% for user-dependent and 62% to 96% for user-independent categories.
Technology and Construction Management
Accelerometer, Activity recognition, Construction, Gyroscope, Machine learning, Neural networks, Productivity analysis, Smartphone sensors, Workers
Akhavian, Reza, and Amir H. Behzadan. "Smartphone-based construction workers' activity recognition and classification." Automation in Construction 71 (2016): 198-209.
Automation in Construction