# Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals
## 4 Research on and Application of Multimodal Physiological Signals
### Multiple Physiological Signals
It was found that the characteristics of different emotions on the same day were inclined to aggregate more closely than the characteristics of the same emotion on different days. They also presented new features and algorithms to deal with the daily variations. [PERSON] et al. [74] presented an approach to establish a real-time emotion detector that included EMG, heart rate and galvanic skin response for application in entertainment and video viewing. They intended to detect the emotional effects of a video in a new manner by recognizing emotional states in the motion stream and by providing the associated binary valence of each detected state. [PERSON] et al. [75] built a user-independent system of emotion recognition based on ECG, EDA and skin temperature variations that could reflect the effect of emotions on the nervous system. They designed an approach with preprocessing and extraction features to draw the emotion-specific features from signals with short segments, and used a support vector machine as a pattern classifier to solve the classification problems, i.e., large overlap among clusters and large variance within clusters. [PERSON] et al. [76] studied how positive, neutral and negative feedback responses from an intelligent tutoring system based on EDA, EMG and ECG signals affect learners' physiology and emotions. The results showed that learners mostly presented expressions of delight when receiving positive feedback, but expressions of surprise when receiving negative feedback. [PERSON] et al. [77] presented an approach to determine operators' emotional states attributed to a set of CAD design tasks based on EEG, galvanic skin resistance, and ECG signals. The stimulation and interpretation of each operator's emotions were performed successfully, through various task action chains, with obvious correlations confirmed between the operator's CAD experience and their emotional states. [PERSON] et al. [78] processed ECG, EMG and EDA signals with unimodal and multimodal methods to develop a physiological model of emotions in 55 subjects. The results showed that the ECG signal was the best in emotion classification, and fusion of all signals could provide the most effective emotion identification. [PERSON] et al. [79] developed a novel approach for valence-arousal model emotion assessment with multiple physiological signals to determine human emotional states using various algorithms. It was found that the deep neural network approach could precisely identify human emotional states. [PERSON] et al. [80] presented a model using a machine learning method for the calibration of the mood of the song according to audio signals and human emotions based on EEG, ECG, EDA and pulse wave signals. They performed extensive experiments using songs that induced different moods and emotions and tried to detect emotions with a certain degree of efficiency and accuracy. [PERSON] et al. [81] built a system of emotion recognition based on the EEG, ECG and EMG signals and used the T method of the Mahalanobis-Taguchi system to handle the signals and further identify emotional states. The results indicated that, although the T method only modified the accuracy on the valence state, it still identified the emotion intensity in different states. [PERSON] et al. [82] studied a model of emotion recognition and judgment based on ECG, EDA, facial EMG and pulse wave signals monitored in 40 subjects playing a computer game. The subjects were required to complete a questionnaire including typical scenarios appearing in the game and subjects' evaluations of the scenarios. Relationships between the signal features and emotion states were established with a statistical approach and the questionnaire responses.