基于生理信号的实时情感识别系统设计与实现

Xin Liu; Man Li Zhong; Yan Fei Lin; Zhi Wen Liu

doi:10.15918/j.tbit1001-0645.2019.s1.032

基于生理信号的实时情感识别系统设计与实现

Translated title of the contribution: Design and Implementation of a Real-time Emotion Recognition System Based on Physiological signals

Xin Liu, Man Li Zhong, Yan Fei Lin^*, Zhi Wen Liu

^*Corresponding author for this work

School of Integrated Circuits and Electronics

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

In this research, we designed and implemented a real-time emotion recognition system by using physiological signals. The video materials were used to stimulate subjects'emotions of happiness, surprise, sadness, anger, fear and calmness. The MP160 physiological recorder was used to collect the physiological signals of ECG, myoelectricity, skin conductance, respiration and skin temperature of the subjects under the each emotion condition. After preprocessing, the combined algorithm including PCA and SVM was used to realize real-time classification of emotions. Finally, the system took four students as the subjects of the experiments, and the average recognition rate of the six emotions was 70%.

Translated title of the contribution	Design and Implementation of a Real-time Emotion Recognition System Based on Physiological signals
Original language	Chinese (Traditional)
Pages (from-to)	176-180
Number of pages	5
Journal	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume	39
DOIs	https://doi.org/10.15918/j.tbit1001-0645.2019.s1.032
Publication status	Published - 1 Jun 2019

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title = "基于生理信号的实时情感识别系统设计与实现",

abstract = "In this research, we designed and implemented a real-time emotion recognition system by using physiological signals. The video materials were used to stimulate subjects'emotions of happiness, surprise, sadness, anger, fear and calmness. The MP160 physiological recorder was used to collect the physiological signals of ECG, myoelectricity, skin conductance, respiration and skin temperature of the subjects under the each emotion condition. After preprocessing, the combined algorithm including PCA and SVM was used to realize real-time classification of emotions. Finally, the system took four students as the subjects of the experiments, and the average recognition rate of the six emotions was 70%.",

keywords = "Classification, Emotion recognition, Feature extraction, Physiological signals",

author = "Xin Liu and Zhong, {Man Li} and Lin, {Yan Fei} and Liu, {Zhi Wen}",

year = "2019",

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doi = "10.15918/j.tbit1001-0645.2019.s1.032",

language = "繁体中文",

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T1 - 基于生理信号的实时情感识别系统设计与实现

AU - Liu, Xin

AU - Zhong, Man Li

AU - Lin, Yan Fei

AU - Liu, Zhi Wen

PY - 2019/6/1

Y1 - 2019/6/1

N2 - In this research, we designed and implemented a real-time emotion recognition system by using physiological signals. The video materials were used to stimulate subjects'emotions of happiness, surprise, sadness, anger, fear and calmness. The MP160 physiological recorder was used to collect the physiological signals of ECG, myoelectricity, skin conductance, respiration and skin temperature of the subjects under the each emotion condition. After preprocessing, the combined algorithm including PCA and SVM was used to realize real-time classification of emotions. Finally, the system took four students as the subjects of the experiments, and the average recognition rate of the six emotions was 70%.

AB - In this research, we designed and implemented a real-time emotion recognition system by using physiological signals. The video materials were used to stimulate subjects'emotions of happiness, surprise, sadness, anger, fear and calmness. The MP160 physiological recorder was used to collect the physiological signals of ECG, myoelectricity, skin conductance, respiration and skin temperature of the subjects under the each emotion condition. After preprocessing, the combined algorithm including PCA and SVM was used to realize real-time classification of emotions. Finally, the system took four students as the subjects of the experiments, and the average recognition rate of the six emotions was 70%.

KW - Classification

KW - Emotion recognition

KW - Feature extraction

KW - Physiological signals

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SN - 1001-0645

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JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology

JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology

ER -

基于生理信号的实时情感识别系统设计与实现

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