Exploiting Emotion Recognition Models to Automate Pain Level Classification

Traditional pain assessment methods heavily rely on patient self-reporting, which is often unreliable because different people have different pain baselines, and some people may not even have verbal communication ability. To address this limitation, researchers utilize facial expressions to decipher the pain level. Unfortunately, the accuracy of such methods is challenged by many factors, such as the angle between the subject’s face and the camera, the interference from the subject’s surrounding environment, and the mixed motion of the subject. To improve the accuracy and automate the detection of pain levels, this work proposes a hardware-software collaborative framework that employs multiple emotion recognition models to perform explainable machine learning algorithms for pain detection. The emotion recognition based on the Deepface learning model facilitates us to identify the top five Action Units (AUs) that are highly related to pain. Our case studies with the MIntPain database show that the proposed method can cause the false negative rate to diminish to zero. Our experimental results also indicate that the Pearson correlation coefficient between emotion categories and the pain level is not a reliable metric to distinguish a specific pain level.