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Research Trends of Human–Computer Interaction Studies in Construction Hazard Recognition: A Bibliometric Review
Human–computer interaction, an interdisciplinary discipline, has become a frontier research topic in recent years. In the fourth industrial revolution, human–computer interaction has been increasingly applied to construction safety management, which has significantly promoted the progress of hazard...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471763/ https://www.ncbi.nlm.nih.gov/pubmed/34577380 http://dx.doi.org/10.3390/s21186172 |
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author | Wang, Jiaming Cheng, Rui Liu, Mei Liao, Pin-Chao |
author_facet | Wang, Jiaming Cheng, Rui Liu, Mei Liao, Pin-Chao |
author_sort | Wang, Jiaming |
collection | PubMed |
description | Human–computer interaction, an interdisciplinary discipline, has become a frontier research topic in recent years. In the fourth industrial revolution, human–computer interaction has been increasingly applied to construction safety management, which has significantly promoted the progress of hazard recognition in the construction industry. However, limited scholars have yet systematically reviewed the development of human–computer interaction in construction hazard recognition. In this study, we analyzed 274 related papers published in ACM Digital Library, Web of Science, Google Scholar, and Scopus between 2000 and 2021 using bibliometric methods, systematically identified the research progress, key topics, and future research directions in this field, and proposed a research framework for human–computer interaction in construction hazard recognition (CHR-HCI). The results showed that, in the past 20 years, the application of human–computer interaction not only made significant contributions to the development of hazard recognition, but also generated a series of new research subjects, such as multimodal physiological data analysis in hazard recognition experiments, development of intuitive devices and sensors, and the human–computer interaction safety management platform based on big data. Future research modules include computer vision, computer simulation, virtual reality, and ergonomics. In this study, we drew a theoretical map reflecting the existing research results and the relationship between them, and provided suggestions for the future development of human–computer interaction in the field of hazard recognition from a practical perspective. |
format | Online Article Text |
id | pubmed-8471763 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84717632021-09-28 Research Trends of Human–Computer Interaction Studies in Construction Hazard Recognition: A Bibliometric Review Wang, Jiaming Cheng, Rui Liu, Mei Liao, Pin-Chao Sensors (Basel) Review Human–computer interaction, an interdisciplinary discipline, has become a frontier research topic in recent years. In the fourth industrial revolution, human–computer interaction has been increasingly applied to construction safety management, which has significantly promoted the progress of hazard recognition in the construction industry. However, limited scholars have yet systematically reviewed the development of human–computer interaction in construction hazard recognition. In this study, we analyzed 274 related papers published in ACM Digital Library, Web of Science, Google Scholar, and Scopus between 2000 and 2021 using bibliometric methods, systematically identified the research progress, key topics, and future research directions in this field, and proposed a research framework for human–computer interaction in construction hazard recognition (CHR-HCI). The results showed that, in the past 20 years, the application of human–computer interaction not only made significant contributions to the development of hazard recognition, but also generated a series of new research subjects, such as multimodal physiological data analysis in hazard recognition experiments, development of intuitive devices and sensors, and the human–computer interaction safety management platform based on big data. Future research modules include computer vision, computer simulation, virtual reality, and ergonomics. In this study, we drew a theoretical map reflecting the existing research results and the relationship between them, and provided suggestions for the future development of human–computer interaction in the field of hazard recognition from a practical perspective. MDPI 2021-09-15 /pmc/articles/PMC8471763/ /pubmed/34577380 http://dx.doi.org/10.3390/s21186172 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Wang, Jiaming Cheng, Rui Liu, Mei Liao, Pin-Chao Research Trends of Human–Computer Interaction Studies in Construction Hazard Recognition: A Bibliometric Review |
title | Research Trends of Human–Computer Interaction Studies in Construction Hazard Recognition: A Bibliometric Review |
title_full | Research Trends of Human–Computer Interaction Studies in Construction Hazard Recognition: A Bibliometric Review |
title_fullStr | Research Trends of Human–Computer Interaction Studies in Construction Hazard Recognition: A Bibliometric Review |
title_full_unstemmed | Research Trends of Human–Computer Interaction Studies in Construction Hazard Recognition: A Bibliometric Review |
title_short | Research Trends of Human–Computer Interaction Studies in Construction Hazard Recognition: A Bibliometric Review |
title_sort | research trends of human–computer interaction studies in construction hazard recognition: a bibliometric review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471763/ https://www.ncbi.nlm.nih.gov/pubmed/34577380 http://dx.doi.org/10.3390/s21186172 |
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