Classifying Emotional States Using Data Collected from Wearable Devices

In this paper we evaluate the performance of four machine learning algorithms (i.e., Gradient Boosting, Random Forest, Decision Trees and K-Nearest Neighbors) for classifying emotional states and detecting stress using physiological data collected from chest and wrist wearable devices. We have performed a comprehensive analysis involving feature extraction and selection from physiological data, followed by hyperparameter tuning for each algorithm. The performance of the resulting models has been assessed using the accuracy, recall, F1 score, and loss metrics, alongside confusion matrices to provide insights into the effectiveness of classification. The paper analyzes how well each algorithm classifies emotional states, highlighting their strengths and weaknesses.