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Analysis of Home Health Sensor Data

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Handbook of Smart Homes, Health Care and Well-Being

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Abstract

This chapter focuses on the analysis of data that is collected from sensors in the home environment. First we discuss the need for a good model that relates sensor data (or features derived from the data) to indicators of health and well-being. Then we present several methods for model building. We distinguish between supervised methods that need data annotated with the desired health indicators, unsupervised methods that find characteristic patterns by just analyzing large amounts of data, and knowledge-driven methods that use expert knowledge. We discuss the advantages and disadvantages of the different methods.

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Authors and Affiliations

  1. Amsterdam University of Applied Science, Amsterdam, The Netherlands

    Ben Kröse

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  1. Ben Kröse
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Correspondence to Ben Kröse .

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Editors and Affiliations

  1. Fontys University of Applied Sciences, Eindhoven, The Netherlands

    Joost van Hoof

  2. School of Medicine, University of Washington, Seattle, Washington, USA

    George Demiris

  3. Fontys University of Applied Sciences, Eindhoven, The Netherlands

    Eveline J.M. Wouters

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© 2014 Springer International Publishing Switzerland

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Kröse, B. (2014). Analysis of Home Health Sensor Data. In: van Hoof, J., Demiris, G., Wouters, E. (eds) Handbook of Smart Homes, Health Care and Well-Being. Springer, Cham. https://doi.org/10.1007/978-3-319-01904-8_21-1

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  • DOI: https://doi.org/10.1007/978-3-319-01904-8_21-1

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  • Publisher Name: Springer, Cham

  • Online ISBN: 978-3-319-01904-8

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