Art

J-GLOBAL ID：201902281059190746   Reference number：19A0968316

A Comprehensive Study of Activity Recognition Using Accelerometers

加速度計を用いた活動認識の包括的研究【JST・京大機械翻訳】

Author (8)： Twomey Niall (School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK) ,  Diethe Tom (School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK) ,  Diethe Tom (Amazon Research, Cambridge CB3 0RD, UK) ,  Fafoutis Xenofon (School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK) ,  Elsts Atis (School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK) ,  McConville Ryan (School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK) ,  Flach Peter (School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK) ,  Craddock Ian (School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK)
Material： Informatics (Web)
Volume： 5  Issue： 2  Page： 27  Publication year： 2018 
JST Material Number： U7202A  ISSN： 2227-9709  Document type： Article
Article type： 原著論文  Country of issue： Switzerland (CHE)  Language： ENGLISH (EN)

Abstract/Point： This paper serves as a survey ...

Register(free) or Log In to access     To see more with JDream III (charged).   {{ this.onShowAbsJLink("http://jdream3.com/lp/jglobal/index.html?docNo=19A0968316&from=J-GLOBAL&jstjournalNo=U7202A") }}

Thesaurus term： Data Collection ,  maintenance ,  segmentation ,  reliability (property) ,  Machine Learning ,  *accelerometer ,  sensor ,  test data ,  longitudinal data ,  prediction accuracy
Semi thesaurus term： 予測性能 ,  オーバヘッド ,  最先端技術 ,  *行動認識 , 【Automatic Indexing@JST】

Author keywords (5)： activities of daily living ,  activity recognition ,  accelerometers ,  machine learning ,  sensors

JST classification (1)： Pattern recognition  (JE07000S)

Reference (95)：

• Biddle, S.; Biddle, S.J.H.; Pearson, N.; Ross, G.M.; Braithwaite, R. Tracking of sedentary behaviours of young people: A systematic review. Prev. Med. 2010, 51, 345-351.
• Kwapisz, J.; Weiss, G.; Moore, S. Activity recognition using cell phone accelerometers. ACM SigKDD Explor. Newsl. 2011, 12, 74-82.
• Siirtola, P.; Röning, J. Recognizing Human Activities User-Independently on Smartphones Based on Accelerometer Data. Int. J. Int. Multimed. Artif. Intell. 2012, 1, 38-45.
• Anguita, D.; Ghio, A.; Oneto, L.; Parra, X.; Reyes-Ortiz, J.L. A public domain dataset for human activity recognition using smartphones. In Proceedings of the 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 24-26 April 2013.
• Brezmes, T.; Gorricho, J.L.; Cotrina, J. Activity recognition from accelerometer data on a mobile phone. In Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living; Springer: Heidelberg/Berlin, Germany, 2009; pp. 796-799.

Terms in the title (3)： 加速度計 ,  活動認識 ,  研究