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Detecting anomalies in activities of daily living of elderly residents via energy disaggregation and Cox processes

Detecting anomalies in activities of daily living of elderly residents via energy disaggregation and Cox processes
Detecting anomalies in activities of daily living of elderly residents via energy disaggregation and Cox processes
Monitoring the health of the elderly living independently in their own homes is a key issue in building sustainable healthcare models which support a country’s ageing population. Existing approaches have typically proposed remotely monitoring the behaviour of a household’s occupants through the use of additional sensors. However the costs and privacy concerns of such sensors have significantly limited their potential for widespread adoption. In contrast, in this paper we propose an approach which detects Activities of Daily Living, which we use as a proxy for the health of the household residents. Our approach detects appliance usage from existing smart meter data, from which the unique daily routines of the household occupants are learned automatically via a log Gaussian Cox process. We evaluate our approach using two real-world data sets, and show it is able to detect over 80% of kettle uses while generating less than 10% false positives. Furthermore, our approach allows earlier interventions in households with a consistent routine and fewer false alarms in the remaining households, relative to a fixed-time intervention benchmark.
Alcalá, José
40d4f0bd-707b-4501-b7a7-9a60fecc5eca
Parson, Oliver
9630bcd4-3d91-4b2a-b94a-24bdb84efab6
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Alcalá, José
40d4f0bd-707b-4501-b7a7-9a60fecc5eca
Parson, Oliver
9630bcd4-3d91-4b2a-b94a-24bdb84efab6
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc

Alcalá, José, Parson, Oliver and Rogers, Alex (2015) Detecting anomalies in activities of daily living of elderly residents via energy disaggregation and Cox processes. 2nd ACM International Conference on Embedded Systems For Energy-Efficient Built Environments (ACM BuildSys), Korea, Republic of. 04 - 05 Nov 2015. 10 pp. (doi:10.1145/2821650.2821654).

Record type: Conference or Workshop Item (Paper)

Abstract

Monitoring the health of the elderly living independently in their own homes is a key issue in building sustainable healthcare models which support a country’s ageing population. Existing approaches have typically proposed remotely monitoring the behaviour of a household’s occupants through the use of additional sensors. However the costs and privacy concerns of such sensors have significantly limited their potential for widespread adoption. In contrast, in this paper we propose an approach which detects Activities of Daily Living, which we use as a proxy for the health of the household residents. Our approach detects appliance usage from existing smart meter data, from which the unique daily routines of the household occupants are learned automatically via a log Gaussian Cox process. We evaluate our approach using two real-world data sets, and show it is able to detect over 80% of kettle uses while generating less than 10% false positives. Furthermore, our approach allows earlier interventions in households with a consistent routine and fewer false alarms in the remaining households, relative to a fixed-time intervention benchmark.

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More information

Accepted/In Press date: 1 September 2015
e-pub ahead of print date: 4 November 2015
Published date: 4 November 2015
Venue - Dates: 2nd ACM International Conference on Embedded Systems For Energy-Efficient Built Environments (ACM BuildSys), Korea, Republic of, 2015-11-04 - 2015-11-05
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 381714
URI: https://eprints.soton.ac.uk/id/eprint/381714
PURE UUID: b743cbce-53f7-48b1-9712-31b749966f1f

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Date deposited: 21 Sep 2015 16:18
Last modified: 17 May 2018 16:32

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Contributors

Author: José Alcalá
Author: Oliver Parson
Author: Alex Rogers

University divisions

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