The Workshop on Pain Assessment and Affective Intelligence (PAAIn) brings together researchers working on AI-driven methods for objective pain assessment. The workshop features two complementary challenges: one focused on physiological signals and another on motion-based sensing for pain recognition.
By providing shared datasets and benchmarking tasks, the workshop aims to advance robust machine learning approaches and foster collaboration across affective computing, biomedical sensing, and intelligent healthcare technologies.
Topics of interest include, but are not limited to:
Computational pain assessment from behavioural, physiological, and multimodal signals
Machine learning and deep learning approaches for automatic pain recognition
Pre-training and representation learning for affective and physiological data
Multimodal fusion and cross-modal learning for pain and affect recognition
Subject-independent and cross-dataset generalisation
Temporal modelling of affective and physiological signals (e.g., transformers, sequence models)
Robust, interpretable, and fair affective AI systems
Clinical and real-world applications of affective intelligence
