Smartphone-based construction workers' activity recognition and classification Academic Article

abstract

• 2016 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.

authors

• Behzadan, Amir

published proceedings

• AUTOMATION IN CONSTRUCTION

author list (cited authors)

• Akhavian, R., & Behzadan, A. H.

citation count

• 164

complete list of authors

• Akhavian, Reza||Behzadan, Amir H

publication date

• November 2016

publisher

• Elsevier  Publisher

published in

• Automation in Construction  Journal

keywords

• Accelerometer
• Activity Recognition
• Construction
• Gyroscope
• Machine Learning
• Neural Networks
• Productivity Analysis
• Smartphone Sensors
• Workers

Digital Object Identifier (DOI)

• 10.1016/j.autcon.2016.08.015

start page

• 198

end page

• 209

volume

• 71

issue

• Part 2

URL

• http://dx.doi.org/10.1016/j.autcon.2016.08.015