Gouizi K, Bereksi Reguig F, Maaoui C
Biomedical Electronic Laboratory, Abou bekr Belkaid University, BP 230-13000 Chetouane Tlemcen, Algeria.
J Med Eng Technol. 2011 Aug-Oct;35(6-7):300-7. doi: 10.3109/03091902.2011.601784.
Emotion recognition is one of the great challenges in human-human and human-computer interaction. Accurate emotion recognition would allow computers to recognize human emotions and therefore react accordingly. In this paper, an approach for emotion recognition based on physiological signals is proposed. Six basic emotions: joy, sadness, fear, disgust, neutrality and amusement are analysed using physiological signals. These emotions are induced through the presentation of International Affecting Picture System (IAPS) pictures to the subjects. The physiological signals of interest in this analysis are: electromyogram signal (EMG), respiratory volume (RV), skin temperature (SKT), skin conductance (SKC), blood volume pulse (BVP) and heart rate (HR). These are selected to extract characteristic parameters, which will be used for classifying the emotions. The SVM (support vector machine) technique is used for classifying these parameters. The experimental results show that the proposed methodology provides in general a recognition rate of 85% for different emotional states.
情感识别是人与人以及人机交互中的重大挑战之一。准确的情感识别将使计算机能够识别人类情感并据此做出反应。本文提出了一种基于生理信号的情感识别方法。使用生理信号分析六种基本情感：喜悦、悲伤、恐惧、厌恶、中立和娱乐。通过向受试者展示国际情感图片系统（IAPS）图片来诱发这些情感。该分析中感兴趣的生理信号有：肌电图信号（EMG）、呼吸量（RV）、皮肤温度（SKT）、皮肤电导率（SKC）、血容量脉搏（BVP）和心率（HR）。选择这些信号来提取特征参数，这些参数将用于情感分类。支持向量机（SVM）技术用于对这些参数进行分类。实验结果表明，所提出的方法总体上对不同情感状态的识别率为85%。
