Pain Intensity Estimation by a Self--Taught Selection of Histograms of Topographical Features
This work addresses pain assessment for patients who cannot self-report, such as neonates or those with dementia, but it is incremental as it builds on existing methods like the Prkachin-Solomon score.
The paper tackled the problem of estimating pain intensity from facial images by introducing Histograms of Topographical (HoT) features and a semi-supervised self-taught learning procedure, achieving improved discrimination between pain levels and better generalization across individuals on the UNBC McMaster Shoulder Pain database.
Pain assessment through observational pain scales is necessary for special categories of patients such as neonates, patients with dementia, critically ill patients, etc. The recently introduced Prkachin-Solomon score allows pain assessment directly from facial images opening the path for multiple assistive applications. In this paper, we introduce the Histograms of Topographical (HoT) features, which are a generalization of the topographical primal sketch, for the description of the face parts contributing to the mentioned score. We propose a semi-supervised, clustering oriented self--taught learning procedure developed on the emotion oriented Cohn-Kanade database. We use this procedure to improve the discrimination between different pain intensity levels and the generalization with respect to the monitored persons, while testing on the UNBC McMaster Shoulder Pain database.