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dc.contributor.authorPALOMINO, MARCO
dc.contributor.authorAllen, R
dc.contributor.authorAider, F
dc.contributor.authorTirotto, FA
dc.contributor.authorGiorgi, I
dc.contributor.authorAlexander, H
dc.contributor.authorMasala, G
dc.date.accessioned2022-11-07T11:31:32Z
dc.date.available2022-11-07T11:31:32Z
dc.date.issued2022-09-26
dc.identifier.issn2300-5963
dc.identifier.urihttp://hdl.handle.net/10026.1/19859
dc.description.abstract

For the first time in the history of humanity, the number of people over 65 surpassed those under 5 in 2018. Undoubtedly, older people will play a significant role in the future of the economy and society in general, and technological innovation will be indispensable to support them. Thus, we were interested in learning how home automation could enable older people to live independently for longer. To better understand this, we held focus groups with UK senior citizens in 2021, and we analyzed the data derived from them from the perspective of affective computing. We have trained a machine learning classifier capable of distinguishing moods commonly associated with older adults. We have identified depression, sadness and anger as the most prominent mood states conveyed in our focus groups. Our practical insights can aid the design of strategic choices concerning the wellbeing of the ageing population.

dc.format.extent251-258
dc.language.isoen
dc.publisherPTI
dc.titleThe Mood of the Silver Economy: A Data Science Analysis of the Mood States of Older Adults and the Implications on their Wellbeing
dc.typeconference
plymouth.date-start2022-09-04
plymouth.date-finish2022-09-07
plymouth.volume32
plymouth.conference-name17th Conference on Computer Science and Intelligence Systems
plymouth.publication-statusPublished online
plymouth.journalCommunication Papers of the 17th Conference on Computer Science and Intelligence Systems
dc.identifier.doi10.15439/2022f50
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2022-07-07
dc.rights.embargodate2022-11-10
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.15439/2022f50
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeConference Paper/Proceeding/Abstract


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