Recent Trends in Sensor-based Activity Recognition

Maekawa, Takuya; Xia, Qingxin; Otsuka, Ryoma; Yoshimura, Naoya; Tanigaki, Kei

doi:https://doi.org/10.1109/MDM58254.2023.00018

Recent Trends in Sensor-based Activity Recognition

Maekawa, Takuya; Xia, Qingxin; Otsuka, Ryoma et al.

Proceedings - IEEE International Conference on Mobile Data Management, 2023, 36-38

アクセス数:566件（2025-08-26 02:06 集計）

固定URL: https://hdl.handle.net/11094/92853

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タイトル	Recent Trends in Sensor-based Activity Recognition
著者	NRID 1000050447025 1000050447025 researchmap permalink takumae80 takumae80 Scopus Author ID 7203089461 7203089461 著者名 Maekawa, Takuya
	著者名 Xia, Qingxin
	ORCID 0000-0002-5147-1916 0000-0002-5147-1916 researchmap permalink ryoma_otsuka ryoma_otsuka 著者名 Otsuka, Ryoma
	著者名 Yoshimura, Naoya
	著者名 Tanigaki, Kei
キーワード等	Activity recognition
	datasets
	deep learning
	sensors
内容	Maekawa T., Xia Q., Otsuka R., et al. Recent Trends in Sensor-based Activity Recognition. Proceedings - IEEE International Conference on Mobile Data Management 2023-July, 36 (2023); https://doi.org/10.1109/MDM58254.2023.00018.
抄録	This seminar introduces recent trends in sensor-based activity recognition technology. Technology to recognize human activities using sensors has been a hot topic in the field of mobile and ubiquitous computing for many years. Recent developments in deep learning and sensor technology have expanded the application of activity recognition to various domains such as industrial and natural science fields. However, because activity recognition in the new domains suffers from various real problems such as the lack of sufficient training data and complexity of target activities, new solutions have been proposed for the practical problems in applying activity recognition to real-world applications in the new domains. In this seminar, we introduce recent topics in activity recognition from the viewpoints of (1) recent trends in state-of-the-art machine learning methods for practical activity recognition, (2) recently focused domains for human activity recognition such as industrial and medical domains and their public datasets, and (3) applications of activity recognition to the natural science field, especially in animal behavior understanding.
会議情報	会議名 International Conference on Mobile Data Management 回次 24 主催機関 IEEE 開催期間 July 3rd to July 6th, 2023 開催会場 Singapore Institute of Technology 開催地 Singapore 開催国シンガポール
出版者	Institute of Electrical and Electronics Engineers Inc.
収録物名	Proceedings - IEEE International Conference on Mobile Data Management
ページ	36-38
刊行年月	2023-08-22
言語	英語
ハンドルURL	https://hdl.handle.net/11094/92853
PISSN	15516245
eISSN	23750324
関連情報 (isVersionOf)	DOI (出版社版) https://doi.org/10.1109/MDM58254.2023.00018
関連情報 (isPartOf)	ISBN 9798350341010
関連情報 (isPartOf)	ISBN 9798350341027
アクセス権	open access
権利情報	© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
出版タイプ	Accepted Manuscript
カテゴリ	会議発表論文 Conference Paper

資源タイプ	会議発表論文
ローカル資源タイプ	会議発表論文
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	Proceedings - IEEE International Conference on Mobile Data Management. 2023, p. 36-38
DC.title	Recent Trends in Sensor-based Activity Recognition
DC.creator	Maekawa, Takuya
	Xia, Qingxin
	Otsuka, Ryoma
	Yoshimura, Naoya
	Tanigaki, Kei
DC.publisher	Institute of Electrical and Electronics Engineers Inc.
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2023-08-22
DC.identifier	https://doi.org/10.1109/MDM58254.2023.00018
DC.subject	Activity recognition
	datasets
	deep learning
	sensors
DC.description	Maekawa T., Xia Q., Otsuka R., et al. Recent Trends in Sensor-based Activity Recognition. Proceedings - IEEE International Conference on Mobile Data Management 2023-July, 36 (2023); https://doi.org/10.1109/MDM58254.2023.00018.
DCTERMS.abstract	This seminar introduces recent trends in sensor-based activity recognition technology. Technology to recognize human activities using sensors has been a hot topic in the field of mobile and ubiquitous computing for many years. Recent developments in deep learning and sensor technology have expanded the application of activity recognition to various domains such as industrial and natural science fields. However, because activity recognition in the new domains suffers from various real problems such as the lack of sufficient training data and complexity of target activities, new solutions have been proposed for the practical problems in applying activity recognition to real-world applications in the new domains. In this seminar, we introduce recent topics in activity recognition from the viewpoints of (1) recent trends in state-of-the-art machine learning methods for practical activity recognition, (2) recently focused domains for human activity recognition such as industrial and medical domains and their public datasets, and (3) applications of activity recognition to the natural science field, especially in animal behavior understanding.
DC.rights	© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DC.format	92853
citation_title	Recent Trends in Sensor-based Activity Recognition
citation_author	Maekawa, Takuya
	Xia, Qingxin
	Otsuka, Ryoma
	Yoshimura, Naoya
	Tanigaki, Kei
citation_publisher	Institute of Electrical and Electronics Engineers Inc.
citation_language	英語
citation_date	2023-08-22
citation_journal_title	Proceedings - IEEE International Conference on Mobile Data Management
citation_firstpage	36
citation_lastpage	38
citation_doi	https://doi.org/10.1109/MDM58254.2023.00018
citation_public_url	https://hdl.handle.net/11094/92853

Recent Trends in Sensor-based Activity Recognition

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