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Design and development of multimodal analysis system based on biometric signals

机译:基于生物识别信号的多峰分析系统的设计与开发

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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.
机译:在本文中,我们介绍了基于生物识别信号并适用于内容的多模式界面和分析系统。多模式界面包括生物特征分析模块,用于分析和识别人类生物特征信号模式。生物特征多模式界面可以通过分析ECG(心电图)和EEG(脑电图)模式来识别用户的情绪和注意力状态。脑电图分析系统利用5个基本信号值来预测用户的集中状态:MID_BETA,THETA,ALPHA,DELTA和GAMMA信号。为了识别用户的心电图信号模式,应用了基于K均值的EM算法。在情绪识别中,神经情绪的准确性最高,三种情绪的准确性在55.8%至75.1%之间。压力识别显示出83.2%的准确度的高性能结果。

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