The influences of emotional intensity for happiness and sadness on walking

Matthieu Destephe, Takayuki Maruyama, Massimiliano Zecca, Kenji Hashimoto, Atsuo Takanishi

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

16 Citations (Scopus)

Abstract

Walking is one of the most common activities that we perform every day. Even if the main goal of walking is to move from one place to another place, walking can also convey emotional clues in social context. Those clues can be used to improve interactions or any messages we want to express. However, there are not many studies on the effects of the intensity of the emotions on the walking. In this paper, the authors propose to assess the differences between the expression of emotion regarding the expressed intensity (low, middle, high and exaggerated). We observed two professional actors perform emotive walking, with different intensities and we analyzed the recorded data. For each emotion, we analyzed characteristic features which can be used in the future to model gait patterns and to recognize emotions from the gait parameters. Additionally, we found characteristics which can be used to create new emotion expression for our biped robot Kobian, improving the human-robot interaction.

Original languageEnglish
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Pages7452-7455
Number of pages4
DOIs
Publication statusPublished - 2013 Oct 31
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: 2013 Jul 32013 Jul 7

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Country/TerritoryJapan
CityOsaka
Period13/7/313/7/7

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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