Neural network based emotion estimation using heart rate variability and skin resistance

Authors:
Sun K. Yoo;Chung K. Lee;Youn J. Park;Nam H. Kim;Byung C. Lee;Kee S. Jeong
Affiliations:
Dept. of Medical Engineering, College of Medicine Yonsei University, Seoul, Korea;Graduate School in Biomedical Engineering, Center for Emergency Medical Informatics, College of Medicine Yonsei University, Seoul, Korea;Graduate School in Biomedical Engineering, Human Identification Research Center, College of Medicine Yonsei University, Seoul, Korea;Dept. of Medical Engineering, College of Medicine Yonsei University, Seoul, Korea;Dept. of Medical Information System., Yongin Songdam College, Gyeonggi, Korea;Dept. of Medical Information System., Yongin Songdam College, Gyeonggi, Korea
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

In order to build a human-computer interface that is sensitive to a user's expressed emotion, we propose a neural network based emotion estimation algorithm using heart rate variability (HRV) and galvanic skin response (GSR). In this study, a video clip method was used to elicit basic emotions from subjects while electrocardiogram (ECG) and GSR signals were measured. These signals reflect the influence of emotion on the autonomic nervous system (ANS). The extracted features that are emotion-specific characteristics from those signals are applied to an artificial neural network in order to recognize emotions from new signal collections. Results show that the proposed method is able to accurately distinguish a user's emotion.