TY - GEN
T1 - EmotionSense
T2 - 4th IEEE SmartWorld, 15th IEEE International Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
AU - Zhao, Bobo
AU - Wang, Zhu
AU - Yu, Zhiwen
AU - Guo, Bin
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/4
Y1 - 2018/12/4
N2 - In this paper, we develop an automatic emotion recognition system based on a sensor-enriched wearable wristband. Specifically, in order to obtain physiological data from participants, we first adopt a video induction method which can spontaneously evoke human emotions in a real-life environment. Meanwhile, a questionnaire is designed to record the emotion status of the participants, which can be used as the ground-truth for emotion recognition. Second, we collect multi-modal physiological signals by utilizing three different biosensors (including blood volume pause, electrodermal activity, and skin temperature) embedded in the Empatica E4 wristband. Furthermore, we extract time, frequency and nonlinear features from the collected physiological signals, and adopt the sequence forward floating selection (SFFS) method to search for the best emotion-related features. Finally, we classify different emotions base on SVM using the selected features in aspect of arousal, valence, and four emotions. An overall accuracy of 76% for 15 participants demonstrates that the proposed system can recognize human emotions effectively.
AB - In this paper, we develop an automatic emotion recognition system based on a sensor-enriched wearable wristband. Specifically, in order to obtain physiological data from participants, we first adopt a video induction method which can spontaneously evoke human emotions in a real-life environment. Meanwhile, a questionnaire is designed to record the emotion status of the participants, which can be used as the ground-truth for emotion recognition. Second, we collect multi-modal physiological signals by utilizing three different biosensors (including blood volume pause, electrodermal activity, and skin temperature) embedded in the Empatica E4 wristband. Furthermore, we extract time, frequency and nonlinear features from the collected physiological signals, and adopt the sequence forward floating selection (SFFS) method to search for the best emotion-related features. Finally, we classify different emotions base on SVM using the selected features in aspect of arousal, valence, and four emotions. An overall accuracy of 76% for 15 participants demonstrates that the proposed system can recognize human emotions effectively.
KW - Arousal
KW - Blood volume pause
KW - Electrodermal activity
KW - Emotion recognition
KW - Skin temperature
KW - Valence
KW - Wearable wristband
UR - http://www.scopus.com/inward/record.url?scp=85060302201&partnerID=8YFLogxK
U2 - 10.1109/SmartWorld.2018.00091
DO - 10.1109/SmartWorld.2018.00091
M3 - 会议稿件
AN - SCOPUS:85060302201
T3 - Proceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
SP - 346
EP - 355
BT - Proceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
A2 - Loulergue, Frederic
A2 - Wang, Guojun
A2 - Bhuiyan, Md Zakirul Alam
A2 - Ma, Xiaoxing
A2 - Li, Peng
A2 - Roveri, Manuel
A2 - Han, Qi
A2 - Chen, Lei
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 October 2018 through 11 October 2018
ER -