The PainMonit Database: An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition

Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses. Although machine learning models have shown promise in improving the current gold standard of pain monitoring (self-reports) only a handful of datasets are freely accessible to researchers. The PainMonit Database was created for automated pain detection using physiological data. The dataset consists of two parts, as pain can be perceived differently depending on its underlying cause. On the one hand, pain was triggered by artificially applied heat stimuli in an experimental study protocol. On the other hand, physiological responses to pain during a physiotherapy session were also recorded.