Physiological Data Analysis Framework for Pain Prediction in Physical Rehabilitation

Predicting pain in physical rehabilitation remains challenging due to the subjectivity inherent in pain assessment and the high variability among patients. In addition, traditional self-reported scales often introduce bias that complicates objective monitoring. Researchers have explored physiological biomarkers such as heart rate variability (HRV) and photoplethysmography (PPG) for pain assessment, combining these with artificial intelligence to enhance accuracy. Limited datasets and significant inter-individual variability also restrict the practical application of these approaches. This study evaluates the performance of linear regression and random forest models for pain prediction using HRV and oxygenation data. The random forest model was evaluated in several configurations, achieving a classification accuracy of 97.77% for detecting low pain levels. Other configurations yielded overall accuracies of 60.65% and 76.64%, highlighting variations in performance. Notably, the high accuracy in identifying low pain suggests that this approach can reliably detect even mild discomfort at an early stage, which is essential for timely therapeutic interventions. Future work should incorporate advanced models and expanded datasets for improved generalizability.