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Recognising Daily Functioning Activities in Smart Homes

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Abstract

This paper details an end-to-end system for recognising activity in smart homes for e-care. It discusses the hardware options to be considered when designing the smart home, and the particular decisions takes at the eWALL system. It then considers the necessary signal processing algorithms that turn measurements into metadata describing the context of the care recipient. Since activity recognition implementation and testing need long-term measurements and metadata, a realistic simulator is also built for providing input to the activity recognition module. The activity recognition algorithm utilises two models, one for location estimation and another for activity estimation in the given location. They both give correct recognition for 96 and 94% of the time.

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References

  1. Astaras, S., & Pnevmatikakis, A. (2015). Context extraction in the caring home: Infrastructure and algorithms. In IEEE ICC 2015 Workshop on ICT-enabled services and technologies for eHealth and Ambient Assisted Living (pp. 228–233). London, UK.

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Authors and Affiliations

  1. Multimodal Signal Analytics, Athens Information Technology, 44, Kifisias Ave., 15125, Marousi, Athens, Greece

    Aristodemos Pnevmatikakis

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  1. Aristodemos Pnevmatikakis
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Correspondence to Aristodemos Pnevmatikakis.

Additional information

Part of this work has been carried out in the scope of the EC co-funded project eWALL (FP7-610658).

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Pnevmatikakis, A. Recognising Daily Functioning Activities in Smart Homes. Wireless Pers Commun 96, 3639–3654 (2017). https://doi.org/10.1007/s11277-017-4060-3

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  • DOI: https://doi.org/10.1007/s11277-017-4060-3

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