Automatic pain assessment from face video (continuous pain intensity estimation in adults and newborns) - Egede, Joy Onyekachukwu (2019)
Pain assessment is a very crucial aspect of medical diagnosis as it is symptomatic of many medical conditions. In cases where a patient is unable to self-report pain, pain assessment is done by a clinician via observation of behavioural changes and vital signs. However, this method is highly subjective and discontinuous in time. In order to introduce an objective measure to clinical pain assessment and support real-time pain monitoring, automatic pain recognition models have been proposed but the performance of these models is still limited by the imbalanced and small sample pain data available for training. In addition, there is currently a dearth of information on the usability and impact of such tools in clinical settings. This thesis aims to develop novel computer vision and machine learning techniques that can achieve good pain estimation on small and sparse pain datasets and also explore the applicability of automated pain assessment tools to clinical settings.