Continuous Pain Assessment Using Ensemble Feature Selection from Wearable Sensor Data Sickle cell disease (SCD) is a red blood cell disorder complicated by lifelong issues with pain. Management of SCD related pain is particularly challenging due to its subjective nature. Hence, the development of an objective automatic pain assessment method is critical to pain management in SCD. In this work, we developed a continuous pain assessment model using physiological and body movement sensor signals collected from a wearable wristworn device. Specifically, we implemented ensemble feature selection methods to select robust and stable features extracted from wearable data for better understanding of pain. Our experiments showed that the stability of feature selection methods could be substantially increased by using the ensemble approach. Since different ensemble feature selection methods prefer varying feature subsets for pain estimation, we further utilized stacked generalization to maximize the information usage contained in the selected features from different methods. Using this approach, our best performing model obtained the rootmeansquare error of 1.526 and the Pearson correlation of 0.618 for continuous pain assessment. This indicates that subjective pain scores can be estimated using objective wearable sensor data with high precision.

Pain Detection from Facial Videos Using TwoStage Deep Learning A new method to objectively measure pain using computer vision and machine learning technologies is presented. Our method seeks to capture facial expressions of pain to detect pain, especially when a patients cannot communicate pain verbally. This approach relies on using Facial musclebased Action Units (AUs), defined by the Facial Action Coding System (FACS), that are associated with pain. It is impractical to use human FACS coding experts in clinical settings to perform this task as it is too laborintensive and recent research has sought computerbased solutions to the problem. An effective automated system for performing the task is proposed here in which we develop an endtoend deep learningbased Automated Facial Expression Recognition (AFER) that jointly detects the complete set of painrelated AUs. The facial video clip is processed frame by frame to estimate a vector of AU likelihood values for each frame using a deep convolutional neural network. The AU vectors are concatenated to form a table of AU values for a given video clip. Our results show significantly improved performance compared with those obtained with other known methods.