ResearchResearch paperInterpretability · Robotics & Embodied AI1 source · Oct 7, 2026

Few-Shot Learning for Personalised Automated Pain Assessment

In this work, we evaluate Few-Shot Learning, a sub-area of Meta-Learning, as an approach to personalisation in automated pain assessment.

Pain perception varies substantially across individuals, making it difficult for population-based classifiers to generalise across all subjects in a dataset.
One way to account for subject variability is to train personalised classifiers.
We evaluate our method on the BioVid Pain Database, the SenseEmotion Database, and the PainMonit Experimental Dataset (PMED), reaching 85.75% and 35.49% accuracy on BioVid and 82.37% and 41.88% on SenseEmotion in the binary and multi-class settings under a Leave-One-Subject-Out CV protocol respectively, and 90.47% on PMED, for which only a binary benchmark exists.
Our results suggest that support-conditioned few-shot adaptation can improve average performance under inter-subject variability.

[1]Few-Shot Learning for Personalised Automated Pain Assessment
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 08:52 AM
In this work, we evaluate Few-Shot Learning, a sub-area of Meta-Learning, as an approach to personalisation in automated pain assessment.
Pain perception varies substantially across individuals, making it difficult for population-based classifiers to generalise across all subjects in a dataset.

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