Towards an Online Fuzzy Modeling for Human Internal States Detection - ENSTA Paris - École nationale supérieure de techniques avancées Paris Accéder directement au contenu
Communication Dans Un Congrès Année : 2012

Towards an Online Fuzzy Modeling for Human Internal States Detection

Amir Aly
Connectez-vous pour contacter l'auteur

Résumé

In human-robot interaction, a social intelligent robot should be capable of understanding the emotional internal state of the interacting human so as to behave in a proper manner. The main problem towards this approach is that human internal states can't be totally trained on, so the robot should be able to learn and classify emotional states online. This research paper focuses on developing a novel online incremental learning of human emotional states using Takagi-Sugeno (TS) fuzzy model. When new data is present, a decisive criterion decides if the new elements constitute a new cluster or if they confirm one of the previously existing clusters. If the new data is attributed to an existing cluster, the evolving fuzzy rules of the TS model may be updated whether by adding a new rule or by modifying existing rules according to the descriptive potential of the new data elements with respect to the entire existing cluster centers. However, if a new cluster is formed, a corresponding new TS fuzzy model is created and then updated when new data elements get attributed to it. The subtractive clustering algorithm is used to calculate the cluster centers that present the rules of the TS models. Experimental results show the effectiveness of the proposed method.
Fichier principal
Vignette du fichier
Aly-ICARCV2012.pdf (469.14 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01180243 , version 1 (24-07-2015)

Licence

Domaine public

Identifiants

Citer

Amir Aly, Adriana Tapus. Towards an Online Fuzzy Modeling for Human Internal States Detection. The 11th IEEE International Conference on Control, Automation, Robotics, and Vision (ICARCV), Dec 2012, Guangzhou, China. ⟨10.1109/ICARCV.2012.6485379⟩. ⟨hal-01180243⟩

Collections

ENSTA ENSTA_U2IS
23 Consultations
86 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More