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Sensors 2017, 17(5), 1034; doi:10.3390/s17051034

Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data

1
The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Pisa 56025, Italy
2
Alcove Limited, 44 Westbridge Road, London SW11 3PW, UK
3
Bristol Robotics Laboratory, University of West England, Bristol BS16 1QY, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Ioannis Kompatsiaris, Thanos G. Stavropoulos and Antonis Bikakis
Received: 17 March 2017 / Revised: 27 April 2017 / Accepted: 1 May 2017 / Published: 4 May 2017
(This article belongs to the Special Issue Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health)
View Full-Text   |   Download PDF [1754 KB, uploaded 4 May 2017]   |  

Abstract

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. View Full-Text
Keywords: behavioural models; unsupervised machine learning; cognitive health assessment; real-home settings behavioural models; unsupervised machine learning; cognitive health assessment; real-home settings
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MDPI and ACS Style

Fiorini, L.; Cavallo, F.; Dario, P.; Eavis, A.; Caleb-Solly, P. Unsupervised Machine Learning for Developing Personalised Behaviour Models Using Activity Data. Sensors 2017, 17, 1034.

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