Deep Emotion Recognition using Wearable Sensors

Emotion recognition is an emerging interdisciplinary field that integrates methodologies from affective computing, sentiment analysis, signal processing, and machine learning. This project focuses on classifying emotions such as amusement and stress using physiological signals like heart rate and skin conductivity, collected via wearables. To build the model, a hybrid approach was employed that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The model achieved an accuracy rate of 92% under a 5-fold cross-validation setting using grid search and Bayesian optimization for hyperparameter tuning.