Pain Assessment Through Physiological Signals

Pain is subjective and hard to define. It is currently measured by self-reports. However, it is not ideal since not every person can report pain. Developing wearable devices that can automatically detect pain is crucial, but to achieve these devices it is necessary to consider machine learning (ML) models that can classify pain. This work aims to train ML models to perform pain recognition and another to estimate pain intensity using physiological signals. The hyperparameters of each model were tuned using Nested Cross-validation. The Random Forest Classifier was found to be the model that performed better for pain detection with an F1-score of 99.32% in the test set as the Gradient Boost Regressor was considered the best model for pain intensity estimation with a Mean Absolute Error of 0.24 in the test set. As the results obtained are promising, this work motivates further research in this area.