An Exploratory Analysis of Pain Localization via Explainable Computational Modeling
Automatic pain localization, which involves identifying the anatomical origin of pain from peripheral physiological signals without patient self-report, is a clinically critical but largely unaddressed problem, particularly for non-verbal patients. This paper presents a systematic comparison of classical feature engineering and deep sequence learning for subject-independent three-class pain localization using the AI4Pain 2026 Challenge dataset, which comprises four synchronously recorded wearable modalities: electrodermal activity, blood volume pulse, respiration, and peripheral oxygen saturation recorded from 65 participants under controlled TENS-induced pain. A 115-dimensional hand-craf...
| Authors | Ioannis Kyprakis, Stefanos Gkikas, Eric Nichols et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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