An empirical study of machine learning techniques for affect recognition in human–robot interaction

Rani, Pramila, Liu, Changchun, Sarkar, Nilanjan and Vanman, Eric (2006) An empirical study of machine learning techniques for affect recognition in human–robot interaction. Pattern Analysis and Applications, 9 1: 58-69. doi:10.1007/s10044-006-0025-y

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Author Rani, Pramila
Liu, Changchun
Sarkar, Nilanjan
Vanman, Eric
Title An empirical study of machine learning techniques for affect recognition in human–robot interaction
Journal name Pattern Analysis and Applications   Check publisher's open access policy
ISSN 1433-7541
1433-755X
Publication date 2006-05
Sub-type Article (original research)
DOI 10.1007/s10044-006-0025-y
Volume 9
Issue 1
Start page 58
End page 69
Total pages 12
Place of publication London
Publisher Springer
Language eng
Subject 1701 Psychology
090602 Control Systems, Robotics and Automation
Abstract Given the importance of implicit communication in human interactions, it would be valuable to have this capability in robotic systems wherein a robot can detect the motivations and emotions of the person it is working with. Recognizing affective states from physiological cues is an effective way of implementing implicit human–robot interaction. Several machine learning techniques have been successfully employed in affect-recognition to predict the affective state of an individual given a set of physiological features. However, a systematic comparison of the strengths and weaknesses of these methods has not yet been done. In this paper, we present a comparative study of four machine learning methods—K-Nearest Neighbor, Regression Tree (RT), Bayesian Network and Support Vector Machine (SVM) as applied to the domain of affect recognition using physiological signals. The results showed that SVM gave the best classification accuracy even though all the methods performed competitively. RT gave the next best classification accuracy and was the most space and time efficient.
Keyword Affect recognition
Machine learning
Psychophysiology
Emotional robotics
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
School of Psychology Publications
 
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Created: Tue, 03 Mar 2009, 12:45:31 EST by Ms Karen Naughton on behalf of School of Psychology