The goal of the project was to compare the accuracy achieved by different machine learning models designed for sensor-data-based emotion recognition; specifically, physiological (heart rate, temperature) and behavioral (arm movement, hand movement, tone of voice) parameters were considered for classification purposes.

Four emotions were considered:

• Happiness
• Sadness
• Anger
• Fear

The machine learning models employed were:

• Decision tree
• Multilayer perceptron (MLP)
• Residual neural network (ResNet)
• Convolutional neural network (CNN)
• Long short-term memory (LSTM)
• Long short-term memory bidirezionale (BiLSTM)
• Long short-term memory con tre layer (3-layer LSTM)
• Convolutional neural network + Long short-term memory (CNN LSTM)

Different configurations were implemented for each type of machine learning model, each with a different strategy for data normalization and a different dataset for training. The machine learning model that achieved the highest accuracy was the decision tree with a value of 91.47%.

The project directory was divided as follows:

• datasets, containing the datasets used;
• er_utils, which constitutes a package with utility functions;
• logs, within which are the outputs produced by the source execution, so that they can always be consulted and compared with ease;
• models, within which are the patterns resulting from the training of neural networks;
• src, containing the source code of the implemented models -- in particular, directories named after a machine learning model have various configurations of that model within them.