Research and Implementation of Emotion Recognition Platform Based on Multiple Physiological Signals

With the rapid development of artificial intelligence, human-computer interaction, pattern recognition and other technologies, emotion recognition has become a hot topic in this field. Traditional emotional recognition studies mostly use voice features and facial expression image features for recognition, but the external expression features of these emotions are easily subject to subjective control of the human body. However, physiological information is closely related to the cerebral cortex and nerve center of human body, which is objective and authentic. In this paper, four kinds of chaotic characteristic parameters were extracted from ECG(Electrocardiogram), SC(Skin Conductance) and RSP(Respiration), including complexity, box dimension, approximate entropy and information entropy. Three kinds of emotions (Joy, Anger and Sadness) were identified by C4.5 decision tree algorithm. The results of the study show that this method is feasible for emotion recognition. Using C# programming language, Visual Studio integrated development environment (IDE), SQL Server database and other tools, a emotional recognition platform based on multi-physiological information was established, which can extract 12 chaotic characteristic parameters from the collected ECG, SC and RSP. Joy, anger and sadness were recognized through the C4.5 decision tree classifier algorithm, and finally save the information to the local database. This platform includes user login, volunteer management, administrator management, data center and other functional modules to ensure the security and information integrity of the platform. The verification experiment was carried out on the completed platform(In this paper, omit), which proved the effectiveness and practicability of the platform.