arXiv：cs.LG· Heinke Hihn, Ibrahim Eisawy, Patrick Thiam, Hans A. Kestler, Friedhelm Schwenker·· 4 小时前AI 评分27
基于少样本学习实现个性化自动疼痛评估
Few-Shot Learning for Personalised Automated Pain Assessment
AI 导读
研究将少样本学习用于自动疼痛评估的个性化，把从群体级到个体级评估的转变重新解释为任务域迁移，在 BioVid、SenseEmotion 和 PMED 三个数据集上验证。
正文
Abstract: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. In this work, we evaluate Few-Shot Learning, a sub-area of Meta-Learning, as an approach to personalisation in automated pain assessment. We re-interpret the shift from population-level to subject-level evaluation as a task-domain shift, where the observed classes remain fixed but the target subject changes. 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. Using samples to implement k-shot conditioning, the accuracies can be improved to 86.25%, 40.06%, 83.43%, 44.08%, and 91.25%, respectively. To further evaluate the effects and robustness of our method, we provide additional ablation experiments and investigate the personalisation effects. Our results suggest that support-conditioned few-shot adaptation can improve average performance under inter-subject variability.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.09692 [cs.LG] |
| (or arXiv:2610.09692v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09692 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Heinke Hihn [view email]
[v1]
Wed, 7 Oct 2026 08:52:31 UTC (453 KB)
来源：arXiv：cs.LG · arxiv.org