Classification of Human Mental Stress Levels Using a Deep Learning Approach on the K-EmoCon Multimodal Dataset

The idea of "stress stacking" is that the psychological stress many people face in modern society can lead to depression, heart disease, and cancer, among other long-term illnesses. Thus, managing and tracking a person's stress is crucial. This paper proposes that a modified Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model can extract features from Electroencephalogram (EEG), Electrocardiogram (ECG), and Accelerometer (ACC) data. A lengthy feature vector combines relevant features from the various modalities. Combining all of the features could improve classification performance, but it could also increase the number of dimensions and lead to bad performance because of redundant information and inefficiency. This paper uses Kruskal-Wallis analysis to suggest aAnew way to deal with the high-dimensionality problem by automatically finding the best subset of features. To categorize the stress based on the feature vector, we utilized a KNearest Neighborhood (KNN), a Random Forest (RF), a Support Vector Machine (SVM), and a Decision Tree Classifier (DT). SVM outperformed the other three classifiers with a performance accuracy of 94.58%, which is 3.72% Superior to cutting-edge techniques.