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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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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 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.
Keywords: Privacy, deep learning, assisted living, mobile computing, ethics, wearables, dementia, LSTM, RNN
College: Faculty of Science and Engineering
End Page: 47

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