DocumentCode :
3482938
Title :
Conveying emotion intensity with bio-inspired expressive walking - Experiments with sadness and happiness
Author :
Destephe, M. ; Zecca, M. ; Hashimoto, Koji ; Takanishi, A.
Author_Institution :
Grad. Sch. of Sci. & Eng., Waseda Univ., Tokyo, Japan
fYear :
2013
fDate :
26-29 Aug. 2013
Firstpage :
161
Lastpage :
166
Abstract :
The understanding of emotions in humans is really important in the Human-Robot Interaction field. The affective state of a person can be expressed in several ways, one of them being through the way we walk and our gait can convey emotional clues in social context. Those clues can be used to improve the personal interactions with our peers or add meaning to any message we want to express. However, only a few studies in humanoid robotics were done on the effects of the emotions on the walking. In this paper, we propose to assess the emotional walking patterns created from motion capture data with a survey. Those patterns represent different emotions (sadness, happiness) with different intensities (middle, high and exaggerated). Those emotional walking patterns achieved a high recognition rate of the emotions and the subjects (N=13) could recognize whole body emotions without facial expression on our humanoid robot. Additionally, we found out that at first people might perform poorly at recognizing emotions and their intensities but can get better, even without correction or feedback on their performances.
Keywords :
emotion recognition; humanoid robots; bioinspired expressive walking; body emotions; emotion intensity; emotion recognition rate; emotional clues; emotional walking patterns; facial expression; human robot interaction field; humanoid robotics; motion capture data; Educational institutions; Emotion recognition; Face recognition; Humanoid robots; Legged locomotion; Videos;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
RO-MAN, 2013 IEEE
Conference_Location :
Gyeongju
ISSN :
1944-9445
Type :
conf
DOI :
10.1109/ROMAN.2013.6628438
Filename :
6628438
Link To Document :
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