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Conference Papers Year : 2021

A Hierarchical Multi-label classification of Multi-resident activities

Abstract

In this paper, we tackle the problem of daily activities recognition in a multi-resident e-health smart-home using a semi-supervised learning approach based on neural networks. We aim to optimize the recognition task in order to efficiently model the interaction between inhabitants who generally need assistance. Our hierarchical multi-label classification (HMC) approach provides reasoning based on real-world scenarios and a hierarchical representation of the smart space. The performance results prove the efficiency of our proposed model compared with a basic classification task of activities. Mainly, HMC highly improves the classification of interactive activities and increases the overall classification accuracy approximately from 0.627 to 0.831.
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Dates and versions

hal-03562910 , version 1 (09-02-2022)

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Hiba Mehri, Tayeb Lemlouma, Nicolas Montavont. A Hierarchical Multi-label classification of Multi-resident activities. IDEAL 2021: 22nd International Conference on Intelligent Data Engineering and Automated Learning, Nov 2021, Manchester, United Kingdom. pp.76-86, ⟨10.1007/978-3-030-91608-4_8⟩. ⟨hal-03562910⟩
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