A Hierarchical Multi-label classification of Multi-resident activities

Hiba Mehri (1) , Tayeb Lemlouma (2) , Nicolas Montavont (2, 3)

1 IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires
2 OCIF - Objets communicants pour l'Internet du futur
3 IMT Atlantique - SRCD - Département Systèmes Réseaux, Cybersécurité et Droit du numérique

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.

Mots clés: Multi-resident; Recurrent neural networks; LSTM; CNN; Hierarchical multi-label classification; Activity recognition

Domaines: Environnements Informatiques pour l'Apprentissage Humain; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Système multi-agents [cs.MA]; Réseau de neurones [cs.NE]

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⟩