Pain Level Assessment for Infants Using Facial Expression Scores

One of the biggest challenges in pain therapy is the evaluation of pain intensity of patients who cannot express their pain verbally. The measurement of pain is a time-consuming process. Therefore, we investigate the main factors that influence pain in infants. We use facial expressions and random forest to predict pain in the three pain classes aligned with the Premature Infant Pain Scale (PIPP). Using our method, we can classify between low, middle, and high pain with a balanced accuracy of 95.51 percent, setting the new benchmark for three-level pain assessment. Our classifier does not misclassify the highest pain class, which is desirable for the classification of pain. Our algorithm has the potential for creating more time for workers that are constantly under time-pressure.