DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Hyunju | ko |
dc.contributor.author | Kim, Geon | ko |
dc.contributor.author | Lee, Tae-Hoon | ko |
dc.contributor.author | Kim, Kisoo | ko |
dc.contributor.author | Lee, Dongman | ko |
dc.date.accessioned | 2024-09-02T09:00:15Z | - |
dc.date.available | 2024-09-02T09:00:15Z | - |
dc.date.created | 2024-08-29 | - |
dc.date.issued | 2024-05 | - |
dc.identifier.citation | SCIENTIFIC DATA, v.11, no.1 | - |
dc.identifier.uri | http://hdl.handle.net/10203/322530 | - |
dc.description.abstract | As IoT technology advances, using machine learning to detect user activities emerges as a promising strategy for delivering a variety of smart services. It is essential to have access to high-quality data that also respects privacy concerns and data streams from ambient sensors in the surrounding environment meet this requirement. However, despite growing interest in research, there is a noticeable lack of datasets from ambient sensors designed for public spaces, as opposed to those for private settings. To bridge this gap, we design the DOO-RE dataset within an actual meeting room environment, equipped with three types of ambient sensors: those triggered by actuators, users, and the environment itself. This dataset is compiled from the activities of over twenty students throughout a period of four months. DOO-RE provides reliable and purpose-oriented activity data in a public setting, with activity labels verified by multiple annotators through a process of cross-validation to guarantee data integrity. DOO-RE categorizes nine different types of activities and facilitates the study of both single and group activities. We are optimistic that DOO-RE will play a significant role in advancing human activity recognition technologies, enhancing smart automation systems, and enabling the rapid setup of smart spaces through ambient sensors. | - |
dc.language | English | - |
dc.publisher | NATURE PORTFOLIO | - |
dc.title | A dataset of ambient sensors in a meeting room for activity recognition | - |
dc.type | Article | - |
dc.identifier.wosid | 001228889800002 | - |
dc.identifier.scopusid | 2-s2.0-85193936984 | - |
dc.type.rims | ART | - |
dc.citation.volume | 11 | - |
dc.citation.issue | 1 | - |
dc.citation.publicationname | SCIENTIFIC DATA | - |
dc.identifier.doi | 10.1038/s41597-024-03344-7 | - |
dc.contributor.localauthor | Lee, Dongman | - |
dc.contributor.nonIdAuthor | Kim, Geon | - |
dc.contributor.nonIdAuthor | Kim, Kisoo | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
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