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

Steve Williams; John Ware; Berndt Muller

doi:10.1007/978-3-030-12738-1_3

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

Steve Williams, John Ware, Berndt Muller

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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.

Original language	English
Title of host publication	Artificial Intelligence in Health
Subtitle of host publication	First International Workshop, AIH 2018, Stockholm, Sweden, July 13-14, 2018, Revised Selected Papers
Editors	Fernando Koch, Andrew Koster, David Riaño, Sara Montagna, Michael Schumacher, Anette ten Teije, Christian Guttmann, Manfred Reichert, Isabelle Bichindaritz, Pau Herrero, Richard Lenz, Beatriz López, Cindy Marling, Clare Martin, Stefania Montani, Nirmalie Wiratunga
Publisher	Springer
Pages	29-47
ISBN (Electronic)	978-3-030-12738-1
ISBN (Print)	978-3-030-12737-4
DOIs	https://doi.org/10.1007/978-3-030-12738-1_3
Publication status	Published - 21 Feb 2019
Event	First International Workshop, Artificial Intelligence in Health 2018 - Stockholm, Sweden Duration: 13 Jul 2018 → 14 Jul 2018

Publication series

Name	Lecture Notes in Artificial Intelligence
Publisher	Springer
Volume	11326
ISSN (Print)	0302-9743

Workshop

Workshop	First International Workshop, Artificial Intelligence in Health 2018
Abbreviated title	AIH2018
Country/Territory	Sweden
City	Stockholm
Period	13/07/18 → 14/07/18

Keywords

privacy
deep learning
assisted-living
mobile computing
ethics
mHealth
Wearable health
Dementia
safer walking
GPS
LSTM
RNN

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10.1007/978-3-030-12738-1_3Licence: CC BY-NC-ND

SW-MJW-BM-AIH2018-LNAI reviewedII-finalAccepted author manuscript, 3.87 MBLicence: Unspecified

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Williams, S., Ware, J., & Muller, B. (2019). Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia. In F. Koch, A. Koster, D. Riaño, S. Montagna, M. Schumacher, A. T. Teije, C. Guttmann, M. Reichert, I. Bichindaritz, P. Herrero, R. Lenz, B. López, C. Marling, C. Martin, S. Montani, & N. Wiratunga (Eds.), Artificial Intelligence in Health: First International Workshop, AIH 2018, Stockholm, Sweden, July 13-14, 2018, Revised Selected Papers (pp. 29-47). (Lecture Notes in Artificial Intelligence ; Vol. 11326). Springer. https://doi.org/10.1007/978-3-030-12738-1_3

Williams, Steve ; Ware, John ; Muller, Berndt. / Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia. Artificial Intelligence in Health: First International Workshop, AIH 2018, Stockholm, Sweden, July 13-14, 2018, Revised Selected Papers. editor / Fernando Koch ; Andrew Koster ; David Riaño ; Sara Montagna ; Michael Schumacher ; Anette ten Teije ; Christian Guttmann ; Manfred Reichert ; Isabelle Bichindaritz ; Pau Herrero ; Richard Lenz ; Beatriz López ; Cindy Marling ; Clare Martin ; Stefania Montani ; Nirmalie Wiratunga . Springer, 2019. pp. 29-47 (Lecture Notes in Artificial Intelligence ).

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

abstract = "A significant proportion of the population has become used tosharing 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, mHealth, Wearable health, Dementia, safer walking, GPS, LSTM, RNN",

author = "Steve Williams and John Ware and Berndt Muller",

year = "2019",

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doi = "10.1007/978-3-030-12738-1_3",

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editor = "Koch, {Fernando } and Andrew Koster and David Ria{\~n}o and Sara Montagna and Michael Schumacher and Teije, {Anette ten} and Christian Guttmann and Manfred Reichert and Isabelle Bichindaritz and Pau Herrero and Richard Lenz and Beatriz L{\'o}pez and Cindy Marling and Clare Martin and Montani, {Stefania } and {Wiratunga }, Nirmalie",

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Williams, S, Ware, J & Muller, B 2019, Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia. in F Koch, A Koster, D Riaño, S Montagna, M Schumacher, AT Teije, C Guttmann, M Reichert, I Bichindaritz, P Herrero, R Lenz, B López, C Marling, C Martin, S Montani & N Wiratunga (eds), Artificial Intelligence in Health: First International Workshop, AIH 2018, Stockholm, Sweden, July 13-14, 2018, Revised Selected Papers. Lecture Notes in Artificial Intelligence , vol. 11326, Springer, pp. 29-47, First International Workshop, Artificial Intelligence in Health 2018, Stockholm, Sweden, 13/07/18. https://doi.org/10.1007/978-3-030-12738-1_3

Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia. / Williams, Steve; Ware, John; Muller, Berndt.

Artificial Intelligence in Health: First International Workshop, AIH 2018, Stockholm, Sweden, July 13-14, 2018, Revised Selected Papers. ed. / Fernando Koch; Andrew Koster; David Riaño; Sara Montagna; Michael Schumacher; Anette ten Teije; Christian Guttmann; Manfred Reichert; Isabelle Bichindaritz; Pau Herrero; Richard Lenz; Beatriz López; Cindy Marling; Clare Martin; Stefania Montani; Nirmalie Wiratunga . Springer, 2019. p. 29-47 (Lecture Notes in Artificial Intelligence ; Vol. 11326).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia

AU - Williams, Steve

AU - Ware, John

AU - Muller, Berndt

PY - 2019/2/21

Y1 - 2019/2/21

N2 - A significant proportion of the population has become used tosharing 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.

AB - A significant proportion of the population has become used tosharing 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.

KW - privacy

KW - deep learning

KW - assisted-living

KW - mobile computing

KW - ethics

KW - mHealth

KW - Wearable health

KW - Dementia

KW - safer walking

KW - GPS

KW - LSTM

KW - RNN

U2 - 10.1007/978-3-030-12738-1_3

DO - 10.1007/978-3-030-12738-1_3

M3 - Conference contribution

SN - 978-3-030-12737-4

T3 - Lecture Notes in Artificial Intelligence

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BT - Artificial Intelligence in Health

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A2 - Koster, Andrew

A2 - Riaño, David

A2 - Montagna, Sara

A2 - Schumacher, Michael

A2 - Teije, Anette ten

A2 - Guttmann, Christian

A2 - Reichert, Manfred

A2 - Bichindaritz, Isabelle

A2 - Herrero, Pau

A2 - Lenz, Richard

A2 - López, Beatriz

A2 - Marling, Cindy

A2 - Martin, Clare

A2 - Montani, Stefania

A2 - Wiratunga , Nirmalie

PB - Springer

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ER -

Williams S, Ware J, Muller B. Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia. In Koch F, Koster A, Riaño D, Montagna S, Schumacher M, Teije AT, Guttmann C, Reichert M, Bichindaritz I, Herrero P, Lenz R, López B, Marling C, Martin C, Montani S, Wiratunga N, editors, Artificial Intelligence in Health: First International Workshop, AIH 2018, Stockholm, Sweden, July 13-14, 2018, Revised Selected Papers. Springer. 2019. p. 29-47. (Lecture Notes in Artificial Intelligence ). doi: 10.1007/978-3-030-12738-1_3

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

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Keywords

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