A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Smartphone-based construction workers' activity recognition and classification
https://orcid.org/0000-0001-9691-8016
Abstract 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.
Highlights Activity recognition assists in productivity, safety, and simulation analysis. Experiments were conducted in three categories for eight different activities. Pervasive smartphone sensors are used for activity recognition. Five machine learning algorithms were designed to classify different activities. Neural networks had the best performance among the classifiers in all categories.
Smartphone-based construction workers' activity recognition and classification
https://orcid.org/0000-0001-9691-8016
Abstract 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.
Highlights Activity recognition assists in productivity, safety, and simulation analysis. Experiments were conducted in three categories for eight different activities. Pervasive smartphone sensors are used for activity recognition. Five machine learning algorithms were designed to classify different activities. Neural networks had the best performance among the classifiers in all categories.
Smartphone-based construction workers' activity recognition and classification
https://orcid.org/0000-0001-9691-8016
Abstract 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.
Highlights Activity recognition assists in productivity, safety, and simulation analysis. Experiments were conducted in three categories for eight different activities. Pervasive smartphone sensors are used for activity recognition. Five machine learning algorithms were designed to classify different activities. Neural networks had the best performance among the classifiers in all categories.
Smartphone-based construction workers' activity recognition and classification
Akhavian, Reza (author) / Behzadan, Amir H. (author)
Automation in Construction ; 71 ; 198-209
2016-08-13
12 pages
Article (Journal)
Electronic Resource
English
Smartphone-based construction workers' activity recognition and classification
| Online Contents | 2016
Smartphone-based construction workers' activity recognition and classification
| British Library Online Contents | 2016
| Emerald Group Publishing | 2023