Addressing the problems of data-centric physiology-affect relations modeling

Roberto Legaspi, Ken Ichi Fukui, Koichi Moriyama, Satoshi Kurihara, Masayuki Numao, Merlin Suarez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Data-centric affect modeling may render itself restrictive in practical applications for three reasons, namely, it falls short of feature optimization, infers discrete affect classes, and deals with relatively small to average sized datasets. Though it seems practical to use the feature combinations already associated to commonly investigated sensors, there may be other potentially optimal features that can lead to new relations. Secondly, although it seems more realistic to view affect as continuous, it requires using continuous labels that will increase the difficulty of modeling. Lastly, although a large scale dataset reflects a more precise range of values for any given feature, it severely hinders computational efficiency. We address these problems when inferring physiology-affect relations from datasets that contain 2-3 million feature vectors, each with 49 features and labelled with continuous affect values. We employ automatic feature selection to acquire near optimal feature subsets and a fast approximate kNN algorithm to solve the regression problem and cope with the challenge of a large scale dataset. Our results show that high estimation accuracy may be achieved even when the selected feature subset is only about 7% of the original features. May the results here motivate the HCI community to pursue affect modeling without being deterred by large datasets and further the discussions on acquiring optimal features for accurate continuous affect approximation.

Original languageEnglish
Title of host publicationIUI 2010 - Proceedings of the 14th ACM International Conference on Intelligent User Interfaces
Number of pages10
Publication statusPublished - 2010
Externally publishedYes
Event14th ACM International Conference on Intelligent User Interfaces, IUI 2010 - Hong Kong, China
Duration: 2010 Feb 72010 Feb 10

Publication series

NameInternational Conference on Intelligent User Interfaces, Proceedings IUI


Other14th ACM International Conference on Intelligent User Interfaces, IUI 2010
CityHong Kong


  • Affective computing
  • Machine learning
  • Pattern recognition

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction


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