Inferring Health Metrics from Ambient Smart Home Data

Walsh, Lorcan and Kealy, Andrea and Loane, John and Doyle, Julie and Bond, Rodd (2014) Inferring Health Metrics from Ambient Smart Home Data. In: IEEE International Conference on Bioinformatics and Biomedicine, 2014.

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As the population ages, smart home technology and applications are expected to support older adults to age in place and reduce the associated economic and societal burden. This paper describes a study where the relationship between ambient sensors, permanently deployed as part of smart aware apartments, and clinically validated health questionnaires is investigated. 27 sets of ambient data were taken from a 28 day block from 13 participants all of whom were over 60 years old. Features derived from ambient sensor data were found to be significantly correlated to measures of anxiety, sleep quality, depression, loneliness, cognition, quality of life and independent living skills (IADL). Subsequently, linear discriminant analysis was shown to predict participants suffering from increased anxiety and loneliness with a high accuracy (�70%). While the number of participants is small, this study reports that objective ambient features may be used to infer clinically validated health metrics. Such findings may be used to inform interventions for active and healthy ageing.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Smart home technology; Ambient sensors; Older people; Quality of life.
Subjects: Computer Science
Social Sciences > Older people
Research Centres: Netwell/CASALA
Depositing User: Sean McGreal
Date Deposited: 29 Nov 2016 11:49
Last Modified: 29 Nov 2016 11:49
License: Creative Commons: Attribution-Noncommercial-Share Alike 4.0

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