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Sensors in smart homes for independent living of the elderly

Sensors in smart homes for independent living of the elderly
Sensors in smart homes for independent living of the elderly

A rapidly ageing population requires support systems which would enable them to preserve dwellers' independence without compromising on their safety or their quality of life. Smart homes for the elderly have the potential to offer unobtrusive health and wellness monitoring. The aim is to provide a safe, independent living environment which can identify and predict problems by monitoring the activities of daily living (ADLs) of the inhabitants. For this, a system able to handle continuous streams of data is required. Such a system can extract the information by using appropriate classification and learning algorithms and thus allow the remote monitoring of health and wellbeing at a high level. The implementation requires: The use of appropriate sensing technologies, identification of ADLs, data pre-processing techniques and machine learning algorithms. This is challenging due to individual differences: Such a system must be able to personalize individual needs. Our contribution was the design and implementation of a platform to smartly monitor health condition of elderly using sensor data from a smart home, through an interactive user interface which is user-friendly and multi-platform. This proof-of-concept used off-line data, with the view to extend to real-time data collection in the future, which could then be used to inform support providers remotely.

Activity recognition, Elderly, Health Care, Learning Algorithms, Machine Learning, pattern recognition, Smart Home, Smart Homes, Unobtrusive monitoring
Institute of Electrical and Electronics Engineers Inc.
Pirzada, Pireh
ef5cd2e0-16f4-485d-bd1c-4097a82ed123
White, Neil
c7be4c26-e419-4e5c-9420-09fc02e2ac9c
Wilde, Adriana
4f9174fe-482a-4114-8e81-79b835946224
Pirzada, Pireh
ef5cd2e0-16f4-485d-bd1c-4097a82ed123
White, Neil
c7be4c26-e419-4e5c-9420-09fc02e2ac9c
Wilde, Adriana
4f9174fe-482a-4114-8e81-79b835946224

Pirzada, Pireh, White, Neil and Wilde, Adriana (2018) Sensors in smart homes for independent living of the elderly. In 5th International Multi-Topic ICT Conference: Technologies For Future Generations, IMTIC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc.. (doi:10.1109/IMTIC.2018.8467234).

Record type: Conference or Workshop Item (Paper)

Abstract

A rapidly ageing population requires support systems which would enable them to preserve dwellers' independence without compromising on their safety or their quality of life. Smart homes for the elderly have the potential to offer unobtrusive health and wellness monitoring. The aim is to provide a safe, independent living environment which can identify and predict problems by monitoring the activities of daily living (ADLs) of the inhabitants. For this, a system able to handle continuous streams of data is required. Such a system can extract the information by using appropriate classification and learning algorithms and thus allow the remote monitoring of health and wellbeing at a high level. The implementation requires: The use of appropriate sensing technologies, identification of ADLs, data pre-processing techniques and machine learning algorithms. This is challenging due to individual differences: Such a system must be able to personalize individual needs. Our contribution was the design and implementation of a platform to smartly monitor health condition of elderly using sensor data from a smart home, through an interactive user interface which is user-friendly and multi-platform. This proof-of-concept used off-line data, with the view to extend to real-time data collection in the future, which could then be used to inform support providers remotely.

Full text not available from this repository.

More information

Published date: 17 September 2018
Venue - Dates: 5th International Multi-Topic ICT Conference: Technologies For Future Generations, IMTIC 2018, Jamshoro, Pakistan, 2018-04-25 - 2018-04-27
Keywords: Activity recognition, Elderly, Health Care, Learning Algorithms, Machine Learning, pattern recognition, Smart Home, Smart Homes, Unobtrusive monitoring

Identifiers

Local EPrints ID: 425969
URI: https://eprints.soton.ac.uk/id/eprint/425969
PURE UUID: b782d489-e829-4215-9fa0-68539506a925
ORCID for Neil White: ORCID iD orcid.org/0000-0003-1532-6452

Catalogue record

Date deposited: 08 Nov 2018 17:30
Last modified: 09 Nov 2018 01:36

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