TY - JOUR
T1 - Emotion extraction based on multi bio-signal using back-propagation neural network
AU - Yoo, Gilsang
AU - Seo, Sanghyun
AU - Hong, Sungdae
AU - Kim, Hyeoncheol
N1 - Funding Information:
This research was supported by a Korea University Grant with Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01057975).
Publisher Copyright:
© 2016, Springer Science+Business Media New York.
PY - 2018/2/1
Y1 - 2018/2/1
N2 - This study proposes a system that can recognize human emotional state from bio-signal. The technology is provided to improve the interaction between humans and computers to achieve an effective human–machine that is capable for intelligent interaction. The proposed method is able to recognize six emotional states, such as joy, happiness, fear, anger, despair, and sadness. These set of emotional states are widely used for emotion recognition purposes. The result shows that the proposed method can distinguish one emotion compared to all other possible emotional states. The method is composed of two steps: 1) multi-modal bio-signal evaluation and 2) emotion recognition using artificial neural network. In the first step, we present a method to analyze and fix human sensitivity using physiological signals, such as electroencephalogram, electrocardiogram, photoplethysmogram, respiration, and galvanic skin response. The experimental analysis shows that the proposed method has good accuracy performance and could be applied on many human–computer interaction devices for emotion detection.
AB - This study proposes a system that can recognize human emotional state from bio-signal. The technology is provided to improve the interaction between humans and computers to achieve an effective human–machine that is capable for intelligent interaction. The proposed method is able to recognize six emotional states, such as joy, happiness, fear, anger, despair, and sadness. These set of emotional states are widely used for emotion recognition purposes. The result shows that the proposed method can distinguish one emotion compared to all other possible emotional states. The method is composed of two steps: 1) multi-modal bio-signal evaluation and 2) emotion recognition using artificial neural network. In the first step, we present a method to analyze and fix human sensitivity using physiological signals, such as electroencephalogram, electrocardiogram, photoplethysmogram, respiration, and galvanic skin response. The experimental analysis shows that the proposed method has good accuracy performance and could be applied on many human–computer interaction devices for emotion detection.
KW - Artificial neural network
KW - Back propagation
KW - Bio signal
KW - Emotion extraction
UR - http://www.scopus.com/inward/record.url?scp=85001600691&partnerID=8YFLogxK
U2 - 10.1007/s11042-016-4213-5
DO - 10.1007/s11042-016-4213-5
M3 - Article
AN - SCOPUS:85001600691
VL - 77
SP - 4925
EP - 4937
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
SN - 1380-7501
IS - 4
ER -