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Focus Theme: “Machine Learning and Data Analytics in Pervasive Health”
Guest editors: Nuria Oliver, Oscar Mayora, Michael Marschollek
Deadline: July 28, 2017
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Archive (2016–2006)

Expert Knowledge for Modeling Functional Health from Sensor Data

Journal: Methods of Information in Medicine
Subtitle: A journal stressing, for more than 50 years, the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care
ISSN: 0026-1270
Issue: 2016 (Vol. 55): Issue 6 2016
Pages: 516-524
Ahead of Print: 2016-06-20

Expert Knowledge for Modeling Functional Health from Sensor Data

Original Article

Online Supplementary Material

S. M. B. Robben (1), M. C. Pol (2), B. M. Buurman (3), B. J. A. Kröse (1, 4)

(1) Research Group Digital Life, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands; (2) Research Group Occupational Therapy: Participation and the Environment, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands; (3) Department of Internal Medicine, section of Geriatric Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands; (4) Research Group Digital Life, Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands


Telemedicine, ambulatory monitoring, frail elderly, Activities of daily living, telemetry, biomedical technology


Background: ICT based solutions are increasingly introduced for active and healthy ageing. In this context continuous monitoring of older adults with domestic sensor systems has been suggested to provide important information about their functional health. However, there is not yet a solid model for the interpretation of the sensor data.

Objectives: The aim of our study is to define a set of predictors of functional health that can be measured with domestic sensors and to determine thresholds that identify relevant changes in these predictors.

Methods: On the basis of literature we develop a model that relates functional health predictors to features derived from sensor data. The parameters of this model are determined on the basis of a study among health experts (n = 38). The use of the full model is illustrated with three cases.

Results: We identified 25 predictors and their attributes. For 12 of them that can be measured with passive infrared motion sensors we determined their parameters: the attribute thresholds and the urgency thresholds.

Conclusions: With the parametrized predictors in the model, domestic sensors can be deployed to assess functional health in a standardized way. Three case examples showed how the model can be used as a screening instrument for functional decline.

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