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Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data

The goal of this study is to address two major issues that undermine the large scale deployment of smart home sensing solutions in people’s homes. These include the costs associated with having to install and maintain a large number of sensors, and the pragmatics of annotating numerous sensor data s...

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Autores principales: Fiorini, Laura, Cavallo, Filippo, Dario, Paolo, Eavis, Alexandra, Caleb-Solly, Praminda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5469639/
https://www.ncbi.nlm.nih.gov/pubmed/28471405
http://dx.doi.org/10.3390/s17051034
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author Fiorini, Laura
Cavallo, Filippo
Dario, Paolo
Eavis, Alexandra
Caleb-Solly, Praminda
author_facet Fiorini, Laura
Cavallo, Filippo
Dario, Paolo
Eavis, Alexandra
Caleb-Solly, Praminda
author_sort Fiorini, Laura
collection PubMed
description The goal of this study is to address two major issues that undermine the large scale deployment of smart home sensing solutions in people’s homes. These include the costs associated with having to install and maintain a large number of sensors, and the pragmatics of annotating numerous sensor data streams for activity classification. Our aim was therefore to propose a method to describe individual users’ behavioural patterns starting from unannotated data analysis of a minimal number of sensors and a ”blind” approach for activity recognition. The methodology included processing and analysing sensor data from 17 older adults living in community-based housing to extract activity information at different times of the day. The findings illustrate that 55 days of sensor data from a sensor configuration comprising three sensors, and extracting appropriate features including a “busyness” measure, are adequate to build robust models which can be used for clustering individuals based on their behaviour patterns with a high degree of accuracy (>85%). The obtained clusters can be used to describe individual behaviour over different times of the day. This approach suggests a scalable solution to support optimising the personalisation of care by utilising low-cost sensing and analysis. This approach could be used to track a person’s needs over time and fine-tune their care plan on an ongoing basis in a cost-effective manner.
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spelling pubmed-54696392017-06-16 Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data Fiorini, Laura Cavallo, Filippo Dario, Paolo Eavis, Alexandra Caleb-Solly, Praminda Sensors (Basel) Article The goal of this study is to address two major issues that undermine the large scale deployment of smart home sensing solutions in people’s homes. These include the costs associated with having to install and maintain a large number of sensors, and the pragmatics of annotating numerous sensor data streams for activity classification. Our aim was therefore to propose a method to describe individual users’ behavioural patterns starting from unannotated data analysis of a minimal number of sensors and a ”blind” approach for activity recognition. The methodology included processing and analysing sensor data from 17 older adults living in community-based housing to extract activity information at different times of the day. The findings illustrate that 55 days of sensor data from a sensor configuration comprising three sensors, and extracting appropriate features including a “busyness” measure, are adequate to build robust models which can be used for clustering individuals based on their behaviour patterns with a high degree of accuracy (>85%). The obtained clusters can be used to describe individual behaviour over different times of the day. This approach suggests a scalable solution to support optimising the personalisation of care by utilising low-cost sensing and analysis. This approach could be used to track a person’s needs over time and fine-tune their care plan on an ongoing basis in a cost-effective manner. MDPI 2017-05-04 /pmc/articles/PMC5469639/ /pubmed/28471405 http://dx.doi.org/10.3390/s17051034 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Fiorini, Laura
Cavallo, Filippo
Dario, Paolo
Eavis, Alexandra
Caleb-Solly, Praminda
Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data
title Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data
title_full Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data
title_fullStr Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data
title_full_unstemmed Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data
title_short Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data
title_sort unsupervised machine learning for developing personalised behaviour models using activity data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5469639/
https://www.ncbi.nlm.nih.gov/pubmed/28471405
http://dx.doi.org/10.3390/s17051034
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