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

Number of Access:669（2025-10-09 07:51 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/92853

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Title	Recent Trends in Sensor-based Activity Recognition
Creator	NRID 1000050447025 1000050447025 researchmap permalink takumae80 takumae80 Scopus Author ID 7203089461 7203089461 CreatorName Maekawa, Takuya
	CreatorName Xia, Qingxin
	ORCID 0000-0002-5147-1916 0000-0002-5147-1916 researchmap permalink ryoma_otsuka ryoma_otsuka CreatorName Otsuka, Ryoma
	CreatorName Yoshimura, Naoya
	CreatorName Tanigaki, Kei
Subject	Activity recognition
	datasets
	deep learning
	sensors
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.
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.
Conference Information	Conference Name International Conference on Mobile Data Management Conference Sequence 24 Conference Sponsor IEEE Conference Date July 3rd to July 6th, 2023 Conference Venue Singapore Institute of Technology Conference Place Singapore Conference Country Singapore
Publisher	Institute of Electrical and Electronics Engineers Inc.
Source Title	Proceedings - IEEE International Conference on Mobile Data Management
Page	36-38
Date of Issued	2023-08-22
Language	English
Handle URL	https://hdl.handle.net/11094/92853
PISSN	15516245
eISSN	23750324
Relation.isVersionOf	DOI (Publisher Version) https://doi.org/10.1109/MDM58254.2023.00018
Relation.isPartOf	ISBN 9798350341010
Relation.isPartOf	ISBN 9798350341027
Access Rights	open access
CopyRight	© 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.
oaire:version	Accepted Manuscript
Category	会議発表論文 Conference Paper

Resource Type	conference paper
Local Type	Conference Paper
DCMI Type	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	English
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	English
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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