Design and development of multimodal analysis system based on biometric signals

In this paper, we present the multimodal interface and analysis system which is based on biometric signals and applicable to contents. The multimodal interface includes a biometric analysis module that analyzes and recognizes human biometric signal patterns. The biometric multimodal interface can recognize a user´s emotion and concentration status by analyzing ECG(electrocardiogram) and EEG(electroencephalogram) patterns. The electroencephalogram analysis system utilizes 5 basic signal values to predict the concentration status of the user: MID_BETA, THETA, ALPHA, DELTA, and GAMMA signal. To recognize the user´s electrocardiogram signal patterns, K-means-based EM algorithm was applied. In emotion recognition, the neural emotion showed the highest accuracy, and three emotions were in a range of 55.8 to 75.1% accuracy. Stress recognition showed a high performance result of 83.2% accuracy.