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Research on Construction Workers’ Activity Recognition Based on Smartphone

This research on identification and classification of construction workers’ activity contributes to the monitoring and management of individuals. Since a single sensor cannot meet management requirements of a complex construction environment, and integrated multiple sensors usually lack systemic fle...

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Detalles Bibliográficos
Autores principales: Zhang, Mingyuan, Chen, Shuo, Zhao, Xuefeng, Yang, Zhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111560/
https://www.ncbi.nlm.nih.gov/pubmed/30110892
http://dx.doi.org/10.3390/s18082667
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author Zhang, Mingyuan
Chen, Shuo
Zhao, Xuefeng
Yang, Zhen
author_facet Zhang, Mingyuan
Chen, Shuo
Zhao, Xuefeng
Yang, Zhen
author_sort Zhang, Mingyuan
collection PubMed
description This research on identification and classification of construction workers’ activity contributes to the monitoring and management of individuals. Since a single sensor cannot meet management requirements of a complex construction environment, and integrated multiple sensors usually lack systemic flexibility and stability, this paper proposes an approach to construction-activity recognition based on smartphones. The accelerometers and gyroscopes embedded in smartphones were utilized to collect three-axis acceleration and angle data of eight main activities with relatively high frequency in simulated floor-reinforcing steel work. Data acquisition from multiple body parts enhanced the dimensionality of activity features to better distinguish between different activities. The CART algorithm of a decision tree was adopted to build a classification training model whose effectiveness was evaluated and verified through cross-validation. The results showed that the accuracy of classification for overall samples was up to 89.85% and the accuracy of prediction was 94.91%. The feasibility of using smartphones as data-acquisition tools in construction management was verified. Moreover, it was proved that the combination of a decision-tree algorithm with smartphones could achieve complex activity classification and identification.
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spelling pubmed-61115602018-08-30 Research on Construction Workers’ Activity Recognition Based on Smartphone Zhang, Mingyuan Chen, Shuo Zhao, Xuefeng Yang, Zhen Sensors (Basel) Article This research on identification and classification of construction workers’ activity contributes to the monitoring and management of individuals. Since a single sensor cannot meet management requirements of a complex construction environment, and integrated multiple sensors usually lack systemic flexibility and stability, this paper proposes an approach to construction-activity recognition based on smartphones. The accelerometers and gyroscopes embedded in smartphones were utilized to collect three-axis acceleration and angle data of eight main activities with relatively high frequency in simulated floor-reinforcing steel work. Data acquisition from multiple body parts enhanced the dimensionality of activity features to better distinguish between different activities. The CART algorithm of a decision tree was adopted to build a classification training model whose effectiveness was evaluated and verified through cross-validation. The results showed that the accuracy of classification for overall samples was up to 89.85% and the accuracy of prediction was 94.91%. The feasibility of using smartphones as data-acquisition tools in construction management was verified. Moreover, it was proved that the combination of a decision-tree algorithm with smartphones could achieve complex activity classification and identification. MDPI 2018-08-14 /pmc/articles/PMC6111560/ /pubmed/30110892 http://dx.doi.org/10.3390/s18082667 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Mingyuan
Chen, Shuo
Zhao, Xuefeng
Yang, Zhen
Research on Construction Workers’ Activity Recognition Based on Smartphone
title Research on Construction Workers’ Activity Recognition Based on Smartphone
title_full Research on Construction Workers’ Activity Recognition Based on Smartphone
title_fullStr Research on Construction Workers’ Activity Recognition Based on Smartphone
title_full_unstemmed Research on Construction Workers’ Activity Recognition Based on Smartphone
title_short Research on Construction Workers’ Activity Recognition Based on Smartphone
title_sort research on construction workers’ activity recognition based on smartphone
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111560/
https://www.ncbi.nlm.nih.gov/pubmed/30110892
http://dx.doi.org/10.3390/s18082667
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