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Human Stress Recognition by Correlating Vision and EEG Data

Computer Systems Science & Engineering 2023年第6期45卷 2417-2433页

作者： S.Praveenkumar T.Karthick SRM Institute of Science and Technology Department of Data Science and Business SystemsSchool of ComputingKattankulathur603203TamilnaduIndia

Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday *** human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human *** the multimodal dataset DEAP(Database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human *** combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when *** on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress *** the stress identification test,we utilized the DEAP dataset,which included video and EEG *** also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate *** the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG *** Level(FL)fusion that combines the features extracted from video and EEG *** use XGBoost as our classifier model to predict stress,and we put it into *** stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.

关键词： Mental stress physiological data XGBoost feature fusion DEAP video data EEG CNN HAR

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