Interpreting presence sensor data and looking for similarities between homes using cluster analysis

In order to model older people's behaviour in the home we must first understand it. In this paper we examine data from eight purpose-built aware homes over a six-month period, looking at presence in rooms to try to determine patterns amongst the older residents. We look for homes that have simi...

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Bibliographic Details
Published in:	2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops pp. 438 - 445
Main Authors:	Loane, J., O'Mullane, B., Bortz, B., Knapp, R. B.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-05-2011
Subjects:	AAL Aging Cluster Analysis Clustering algorithms Electricity Firing Layout Pattern recognition Pervasive Sensor Data Scatter Plot Silhouette Coefficent
Online Access:	Get full text
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Summary:	In order to model older people's behaviour in the home we must first understand it. In this paper we examine data from eight purpose-built aware homes over a six-month period, looking at presence in rooms to try to determine patterns amongst the older residents. We look for homes that have similar movement patterns using cluster analysis. We also examine how movement over days clusters within individual homes. Our analysis begins to show the possibilities of distinguishing between residents in their homes based on patterns of movement.
ISBN:	9781612847672 1612847676
ISSN:	2153-1633
DOI:	10.4108/icst.pervasivehealth.2011.246114