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International Conference on Engineering Applications of Neural Networks

EANN 2018: Engineering Applications of Neural Networks pp 3–15Cite as

A Framework for Semi-Supervised Adaptive Learning for Activity Recognition in Healthcare Applications

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 893))

Abstract

With the growing popularity of the Internet of Things and connected home products, potential healthcare applications in a smart-home context for assisted living are becoming increasingly apparent. However, challenges in performing real-time human activity recognition (HAR) from unlabelled data and adapting to changing user health remain a major barrier to the practicality of such applications. This paper aims to address these issues by proposing a semi-supervised adaptive HAR system which combines offline and online recognition techniques to provide intelligent real-time support for frequently repeated user activities. The viability of this approach is evaluated by pilot testing it on data from the Aruba CASAS dataset, and additional pilot data collected in the Bristol Robotics Lab’s Assisted Living Studio. The results show that 71% of activity instances were discovered, with an F1-score of 0.93 for the repeating “Meal_Prep” activities. Furthermore, real-time recognition on the collected pilot data occurred near the beginning of the activity 64% of the time and at the halfway point in the activity 96% of the time.

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Author information

Authors and Affiliations

  1. Bristol Robotics Laboratory, University of the West of England, Bristol, BS16 1QY, UK

    Prankit Gupta & Praminda Caleb-Solly

Authors
  1. Prankit Gupta
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  2. Praminda Caleb-Solly
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Correspondence to Prankit Gupta .

Editor information

Editors and Affiliations

  1. University of the West of England, Bristol, United Kingdom

    Elias Pimenidis

  2. Oxford Brookes University, Oxford, United Kingdom

    Chrisina Jayne

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Gupta, P., Caleb-Solly, P. (2018). A Framework for Semi-Supervised Adaptive Learning for Activity Recognition in Healthcare Applications. In: Pimenidis, E., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2018. Communications in Computer and Information Science, vol 893. Springer, Cham. https://doi.org/10.1007/978-3-319-98204-5_1

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  • DOI: https://doi.org/10.1007/978-3-319-98204-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98203-8

  • Online ISBN: 978-3-319-98204-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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