Conference Paper/Proceeding/Abstract 993 views 109 downloads
Preserving safety, privacy and mobility of persons living with Dementia by recognising uncharacteristic out-door movement using Recurrent Neural Networks with low computing capacity
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...
| 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 |
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| ISSN: | 1613-0073 |
| Published: |
AIH 2018 - Joint Workshop on AI in Health
2018
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa43607 |
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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 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. |
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| Keywords: |
privacy, deep learning, assisted-living, mobile computing, ethics, mHeath, wearable health, dementia, safer walking, GPS, LSTM, RNN |
| College: |
Faculty of Science and Engineering |
| Issue: |
01.01.2018 |
| Start Page: |
212 |
| End Page: |
223 |