Can appliances understand the behavior of elderly via machine learning? A feasibility study

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Metadaten
Author:Kun Qian, Tomoya Koike, Kazuhiro Yoshiuchi, Björn SchullerORCiDGND, Yoshiharu Yamamoto
URN:urn:nbn:de:bvb:384-opus4-876960
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/87696
ISSN:2327-4662OPAC
ISSN:2372-2541OPAC
Parent Title (English):IEEE Internet of Things Journal
Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Type:Article
Language:English
Year of first Publication:2021
Publishing Institution:Universität Augsburg
Release Date:2021/06/24
Tag:Signal Processing; Computer Networks and Communications; Hardware and Architecture; Information Systems; Computer Science Applications
Volume:8
Issue:10
First Page:8343
Last Page:8355
DOI:https://doi.org/10.1109/jiot.2020.3045009
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Embedded Intelligence for Health Care and Wellbeing
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht

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