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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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https://hal.archives-ouvertes.fr/hal-03562910
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Submitted on : Wednesday, February 9, 2022 - 12:22:03 PM
Last modification on : Monday, April 4, 2022 - 9:28:24 AM
Long-term archiving on: : Tuesday, May 10, 2022 - 6:41:14 PM

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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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