Key result
Machine learning models using physiological signals detect pain with ~99% accuracy.
Why the study?
Pain is subjective, hard to define, and currently measured by self-reports, which is not ideal since not every person can report pain.
Can machine learning models accurately detect pain and estimate its intensity using physiological signals?
Can machine learning models accurately detect pain and estimate its intensity using physiological signals?
Machine learning models, specifically Random Forest and Gradient Boost Regressor, demonstrate high performance in automatically detecting pain and estimating its intensity from physiological signals.
May support automated pain monitoring; hypothesis-generating and requires prospective validation before clinical use.
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.
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Alves et al. (2024) studied Pain. Machine learning models using physiological signals was evaluated on Pain detection and pain intensity estimation. Machine learning models using physiological signals achieved an F1-score of 99.32% for pain detection and a Mean Absolute Error of 0.24 for pain intensity estimation.
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