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Conference Paper/Proceeding/Abstract 824 views 281 downloads

Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia

Steve Williams, J. Mark Ware, Berndt Müller, Bertie Muller

Artificial Intelligence in Health, Volume: 11326

Swansea University Author: Bertie Muller

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DOI (Published version): 10.1007/978-3-030-12738-1_3

Abstract

A significant proportion of the population has become used to sharing private information on the internet with their friends. This information can leak throughout their social network and the extent that personal information propagates can depend on the privacy policy of large corporations. In an er...

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Published in: Artificial Intelligence in Health
ISBN: 978-3-030-12737-4 978-3-030-12738-1
ISSN: 0302-9743 1611-3349
Published: Springer Nature Switzerland AG 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa48681
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first_indexed 2019-02-26T13:58:53Z
last_indexed 2020-06-16T19:01:12Z
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spelling 2020-06-16T15:52:02.2868083 v2 48681 2019-02-04 Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia a9373756f492363d8453ecf3b828b811 Bertie Muller Bertie Muller true false 2019-02-04 SCS A significant proportion of the population has become used to sharing private information on the internet with their friends. This information can leak throughout their social network and the extent that personal information propagates can depend on the privacy policy of large corporations. In an era of artificial intelligence, data mining, and cloud computing, is it necessary to share personal information with unidentified people? Our research shows that deep learning is possible using relatively low capacity computing. When applied, this demonstrates promising results in spatio-temporal positioning of subjects, in prediction of movement, and assessment of contextual risk. A private surveillance system is particularly suitable in the care of those who may be considered vulnerable. Conference Paper/Proceeding/Abstract Artificial Intelligence in Health 11326 47 Springer Nature Switzerland AG 978-3-030-12737-4 978-3-030-12738-1 0302-9743 1611-3349 Privacy, deep learning, assisted living, mobile computing, ethics, wearables, dementia, LSTM, RNN 10 4 2019 2019-04-10 10.1007/978-3-030-12738-1_3 https://www.springer.com/gp/book/9783030127374 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2020-06-16T15:52:02.2868083 2019-02-04T16:13:39.0828550 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Steve Williams 1 J. Mark Ware 2 Berndt Müller 3 Bertie Muller 4 0048681-26022019091506.pdf 48681.pdf 2019-02-26T09:15:06.3270000 Output 1185774 application/pdf Accepted Manuscript true 2020-02-21T00:00:00.0000000 true eng
title Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia
spellingShingle Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia
Bertie Muller
title_short Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia
title_full Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia
title_fullStr Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia
title_full_unstemmed Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia
title_sort Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia
author_id_str_mv a9373756f492363d8453ecf3b828b811
author_id_fullname_str_mv a9373756f492363d8453ecf3b828b811_***_Bertie Muller
author Bertie Muller
author2 Steve Williams
J. Mark Ware
Berndt Müller
Bertie Muller
format Conference Paper/Proceeding/Abstract
container_title Artificial Intelligence in Health
container_volume 11326
publishDate 2019
institution Swansea University
isbn 978-3-030-12737-4
978-3-030-12738-1
issn 0302-9743
1611-3349
doi_str_mv 10.1007/978-3-030-12738-1_3
publisher Springer Nature Switzerland AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url https://www.springer.com/gp/book/9783030127374
document_store_str 1
active_str 0
description A significant proportion of the population has become used to sharing private information on the internet with their friends. This information can leak throughout their social network and the extent that personal information propagates can depend on the privacy policy of large corporations. In an era of artificial intelligence, data mining, and cloud computing, is it necessary to share personal information with unidentified people? Our research shows that deep learning is possible using relatively low capacity computing. When applied, this demonstrates promising results in spatio-temporal positioning of subjects, in prediction of movement, and assessment of contextual risk. A private surveillance system is particularly suitable in the care of those who may be considered vulnerable.
published_date 2019-04-10T03:59:15Z
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score 11.037581

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