Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 차승현 | - |
dc.date.accessioned | 2022-03-24T01:23:01Z | - |
dc.date.available | 2022-03-24T01:23:01Z | - |
dc.date.issued | 2020-07 | - |
dc.identifier.citation | AUTOMATION IN CONSTRUCTION, v. 119, article no. 103352 | en_US |
dc.identifier.issn | 0926-5805 | - |
dc.identifier.issn | 1872-7891 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0926580520309328?via%3Dihub | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/169373 | - |
dc.description.abstract | Accurate face-to-face interaction estimation is required for a successful data-driven design in workplaces. In previous studies, various sensor-based interaction estimation methods which use proximity and speaking data have been developed. However, these data alone cannot confirm the presence of interactions because non-interacting users also engage in speaking activities. This study aims to develop a novel turn-taking pattern-based interaction estimation (i.e., TIE) framework that integrates turn-taking with location data. The framework estimates interactions in three steps: 1) co-location estimation using a Bluetooth Low Energy beacon; 2) speaking-turn ascertainment through volume-based speaker identification; and 3) interaction group recognition based on turn-taking pattern analysis. Using three different experimental scenarios, the interaction estimation accuracy of the framework was demonstrated to be 77.7%. In the absence of co-location estimation errors, the interaction estimation accuracy increases to 95.5%. The demonstration results indicate that the TIE framework has potential for accurate interaction estimation in workplaces. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1F1A1075915). | en_US |
dc.language.iso | en | en_US |
dc.publisher | ELSEVIER | en_US |
dc.subject | Data-driven design | en_US |
dc.subject | Interaction estimation | en_US |
dc.subject | Human behavior | en_US |
dc.subject | IoT sensor | en_US |
dc.subject | Workplace design | en_US |
dc.title | A human data-driven interaction estimation using IoT sensors for workplace design | en_US |
dc.type | Article | en_US |
dc.relation.volume | 119 | - |
dc.identifier.doi | 10.1016/j.autcon.2020.103352 | - |
dc.relation.page | 1-12 | - |
dc.relation.journal | AUTOMATION IN CONSTRUCTION | - |
dc.contributor.googleauthor | Ma, Jae Hoon | - |
dc.contributor.googleauthor | Cha, Seung Hyun | - |
dc.relation.code | 2020052985 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF HUMAN ECOLOGY[S] | - |
dc.sector.department | DEPARTMENT OF INTERIOR ARCHITECTURE DESIGN | - |
dc.identifier.pid | chash | - |
dc.identifier.researcherID | AAP-2923-2021 | - |
dc.identifier.orcid | https://orcid.org/0000-0002-3426-6865 | - |
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