Adaptive confidence learning for the personalization of pain intensity estimation systems

In this work, a method is presented for the continuous estimation of pain intensity based on fusion of bio-physiological and video features. Furthermore, a method is proposed for the adaptation of the system to unknown test persons based on unlabeled data. First, an analysis is presented that shows which modalities and feature sets are suited best for the task of recognizing pain levels in a person-independent setting. For this, a large set of features is extracted from the available bio-physiological channels (ECG, EMG and skin conductivity) and the video stream. We then propose a method to learn the confidence of a regression system using a multi-stage ensemble classifier. Based on the outcome of the classifier, which is realized by a neural network, confident samples are selected by the adaptation procedure. In various experiments, we show that the algorithm is able to detect highly confident samples which can be used to improve the overall performance. We furthermore discuss the current limitations of automatic pain intensity estimation—in light of the presented approach and beyond.