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dc.contributor.authorRumiantcev, Mikhail
dc.contributor.editorBalandin, Sergey
dc.contributor.editorDeart, Vladimir
dc.contributor.editorTyutina, Tatiana
dc.date.accessioned2021-02-18T08:43:26Z
dc.date.available2021-02-18T08:43:26Z
dc.date.issued2021
dc.identifier.citationRumiantcev, M. (2021). Emotions and Activity Recognition System Using Wearable Device Sensors. In S. Balandin, V. Deart, & T. Tyutina (Eds.), <i>FRUCT '28 : Proceedings of the 28th Conference of Open Innovations Association FRUCT</i> (pp. 381-389). FRUCT Oy. Proceedings of Conference of Open Innovations Association FRUCT. <a href="https://doi.org/10.23919/FRUCT50888.2021.9347652" target="_blank">https://doi.org/10.23919/FRUCT50888.2021.9347652</a>
dc.identifier.otherCONVID_51521743
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/74291
dc.description.abstractNowadays machines have become extremely smart, there are a lot of existing services that seemed to be unexpectable and futuristic decades or even a few years ago. However, artificial intelligence is still far from human intelligence, machines do not have feelings, consciousness, and intuition. How can we help machines to learn about human feelings and understand their needs better? People take their devices wherever they go, what can devices tell us about their owners? Personal preferences and needs are dependent on emotional and situational contexts. Therefore, emotional and activity aware gadgets would be more intuitive and provide more appropriate information to users. Contemporary wearable devices involve wide-ranging sensors. In this paper, I am going to present emotion and activity recognition approaches. The experimental recognition system elaborated during this research, enriched with sensor data collection and machine learning algorithms. It is targeted to guess how users are doing and what they are feeling. Such recognition systems can find applications in different areas such as music recommendations, personal safety or healthcare domains.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherFRUCT Oy
dc.relation.ispartofFRUCT '28 : Proceedings of the 28th Conference of Open Innovations Association FRUCT
dc.relation.ispartofseriesProceedings of Conference of Open Innovations Association FRUCT
dc.rightsCC BY-ND 4.0
dc.subject.otherdeep learning
dc.subject.otherimage segmentation
dc.subject.otherzero-shot semantic segmentation
dc.titleEmotions and Activity Recognition System Using Wearable Device Sensors
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202102181698
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-952-69244-4-1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange381-389
dc.relation.issn2305-7254
dc.type.versionpublishedVersion
dc.rights.copyright© FRUCT Oy, 2021
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceConference of Open Innovations Association
dc.subject.ysopaikkatiedot
dc.subject.ysoanturit
dc.subject.ysoliikkeentunnistus
dc.subject.ysomieliala
dc.subject.ysoälytuotteet
dc.subject.ysotekoäly
dc.subject.ysokasvontunnistus (tietotekniikka)
dc.subject.ysofyysinen aktiivisuus
dc.subject.ysotunteet
dc.subject.ysoälytekniikka
dc.subject.ysotoiminta
dc.subject.ysokoneoppiminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p2152
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jyx.subject.urihttp://www.yso.fi/onto/yso/p22554
jyx.subject.urihttp://www.yso.fi/onto/yso/p2616
jyx.subject.urihttp://www.yso.fi/onto/yso/p26695
jyx.subject.urihttp://www.yso.fi/onto/yso/p23102
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jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
dc.rights.urlhttps://creativecommons.org/licenses/by-nd/4.0/
dc.relation.doi10.23919/FRUCT50888.2021.9347652


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