Automated Pain Assessment Through Facial Expression Using Deep Learning and Image Processing

Abstract

As pain is an unavoidable part of life, this study examines the use of facial expression tech nology in assisting individuals with pain. Accurate pain assessment in health care is essential, especially for non-verbal patients, since conventional methods largely fail because of the in herent subjectivity and self-reporting. Therefore, the present study develops and evaluates an automated pain assessment system through advanced analysis of facial expressions driven by contemporary deep learning techniques. It aims to generate a reliable and unbiased system for detecting and classifying pain intensity. A CNN-based system was developed using base models that apply ResNet-18 and ResNext-50 architectures. A custom-designed final layer was added to optimize classification accuracy, tailored explicitly for pain detection. Comprehensive data preprocessing strategies were used in the model to make it robust; it involved downsam pling and augmentation of the data. It was trained and validated on the UNBC-McMaster Shoulder Pain Expression Archive Database and the Radboud Faces Database, showing an impressive accuracy of over 90% on the training data. However, generalizing the models to unseen validation and test data proved challenging. These findings further articulate the crit ical imperative of enhancing generalisability across diverse patient populations for the system to perform effectively in real-world settings. The results underline the huge potential for deep learning in the automation of pain assessment, while future research remains on better mod eling generalization, promoting integration in clinical settings for a more objective, reliable, and consistent approach to pain management in health care settings.