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Preserving safety, privacy and mobility of persons living with Dementia by recognising uncharacteristic out-door movement using Recurrent Neural Networks with low computing capacity

Steve Williams, J Mark Ware, Bertie Muller

Proceedings of the First Joint Workshop on AI in Health organized as part of the Federated AI Meeting (FAIM 2018), co-located with AAMAS 2018, ICML 2018, IJCAI 2018 and ICCBR 2018, Volume: 2142, Issue: 01.01.2018, Pages: 212 - 223

Swansea University Author: Bertie Muller

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Abstract

A large proportion of the population has become used to sharing private infor- mation on the internet with their friends. This information can leak throughout their social network and the extent that personal information propagates depends on the privacy policy of large corporations. In an era of ar...

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Published in: Proceedings of the First Joint Workshop on AI in Health organized as part of the Federated AI Meeting (FAIM 2018), co-located with AAMAS 2018, ICML 2018, IJCAI 2018 and ICCBR 2018
ISSN: 1613-0073
Published: AIH 2018 - Joint Workshop on AI in Health 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa43607
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first_indexed 2018-08-31T19:44:15Z
last_indexed 2019-01-23T13:56:02Z
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spelling 2019-01-23T10:51:30.3922228 v2 43607 2018-08-31 Preserving safety, privacy and mobility of persons living with Dementia by recognising uncharacteristic out-door movement using Recurrent Neural Networks with low computing capacity a9373756f492363d8453ecf3b828b811 Bertie Muller Bertie Muller true false 2018-08-31 SCS A large proportion of the population has become used to sharing private infor- mation on the internet with their friends. This information can leak throughout their social network and the extent that personal information propagates depends 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 unidentifiable people? Our research shows that deep learning is possible using relatively low capacity computing. The research demonstrates promising results in recognition of human geospatial activity, in prediction of movement, and assessment of contextual risk when applied to spatio-temporal positioning of human subjects. A private surveillance system is thought particularly suitable in the care of those who may, to some, be considered vulnerable. Conference Paper/Proceeding/Abstract Proceedings of the First Joint Workshop on AI in Health organized as part of the Federated AI Meeting (FAIM 2018), co-located with AAMAS 2018, ICML 2018, IJCAI 2018 and ICCBR 2018 2142 01.01.2018 212 223 AIH 2018 - Joint Workshop on AI in Health 1613-0073 privacy, deep learning, assisted-living, mobile computing, ethics, mHeath, wearable health, dementia, safer walking, GPS, LSTM, RNN 13 7 2018 2018-07-13 http://ceur-ws.org/Vol-2142/ COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2019-01-23T10:51:30.3922228 2018-08-31T15:05:31.6674343 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Steve Williams 1 J Mark Ware 2 Bertie Muller 3 0043607-06092018114301.pdf 43607.pdf 2018-09-06T11:43:01.0030000 Output 2503249 application/pdf Accepted Manuscript true 2018-09-06T00:00:00.0000000 true eng
title Preserving safety, privacy and mobility of persons living with Dementia by recognising uncharacteristic out-door movement using Recurrent Neural Networks with low computing capacity
spellingShingle Preserving safety, privacy and mobility of persons living with Dementia by recognising uncharacteristic out-door movement using Recurrent Neural Networks with low computing capacity
Bertie Muller
title_short Preserving safety, privacy and mobility of persons living with Dementia by recognising uncharacteristic out-door movement using Recurrent Neural Networks with low computing capacity
title_full Preserving safety, privacy and mobility of persons living with Dementia by recognising uncharacteristic out-door movement using Recurrent Neural Networks with low computing capacity
title_fullStr Preserving safety, privacy and mobility of persons living with Dementia by recognising uncharacteristic out-door movement using Recurrent Neural Networks with low computing capacity
title_full_unstemmed Preserving safety, privacy and mobility of persons living with Dementia by recognising uncharacteristic out-door movement using Recurrent Neural Networks with low computing capacity
title_sort Preserving safety, privacy and mobility of persons living with Dementia by recognising uncharacteristic out-door movement using Recurrent Neural Networks with low computing capacity
author_id_str_mv a9373756f492363d8453ecf3b828b811
author_id_fullname_str_mv a9373756f492363d8453ecf3b828b811_***_Bertie Muller
author Bertie Muller
author2 Steve Williams
J Mark Ware
Bertie Muller
format Conference Paper/Proceeding/Abstract
container_title Proceedings of the First Joint Workshop on AI in Health organized as part of the Federated AI Meeting (FAIM 2018), co-located with AAMAS 2018, ICML 2018, IJCAI 2018 and ICCBR 2018
container_volume 2142
container_issue 01.01.2018
container_start_page 212
publishDate 2018
institution Swansea University
issn 1613-0073
publisher AIH 2018 - Joint Workshop on AI in Health
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 http://ceur-ws.org/Vol-2142/
document_store_str 1
active_str 0
description A large proportion of the population has become used to sharing private infor- mation on the internet with their friends. This information can leak throughout their social network and the extent that personal information propagates depends 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 unidentifiable people? Our research shows that deep learning is possible using relatively low capacity computing. The research demonstrates promising results in recognition of human geospatial activity, in prediction of movement, and assessment of contextual risk when applied to spatio-temporal positioning of human subjects. A private surveillance system is thought particularly suitable in the care of those who may, to some, be considered vulnerable.
published_date 2018-07-13T03:54:52Z
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score 11.013148

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