Autonomous long-term monitoring system that uses action units to automatically detect pain #
Development of a monitoring system for automatic pain detection using action units.
A special focus in pain research is on reliable pain detection based on changing facial expressions as a form of non-verbal communication and how to clearly differentiate them from those produced by other emotional states. Here we use our SHORE® emotion analysis software and the Facial Action Coding System (FACS) to reliably detect action units.
In collaboration with the University of Bamberg, our goal is to create an autonomous system that can automatically detect pain in patients who are unable to communicate – and do so in a timely manner and when medical staff are not present.
Chair of Applied Computer Sciences / Cognitive Systems at the University of Bamberg
- Using machine learning approaches to interpret preprocessed action units
Chair of Physiological Psychology at the University of Bamberg
- Database for machine learning
- Evaluation of results
Fraunhofer IIS | Image Analysis and Pattern Recognition
- Emotion recognition in a variety of environmental, lighting and perspective contexts, based on reliable detection and identification of action units with the help of machine learning