# An Exploratory Analysis of Pain Localization via Explainable Computational Modeling

arXiv: https://arxiv.org/abs/2607.19726 · HTML: https://mlsanity.com/paper/2607.19726/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 62 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 1 of 1)

- [A Pain Assessment Framework based on multimodal data and Deep Machine Learning methods](https://mlsanity.com/paper/2505.05396/) (2025)

## Abstract (arXiv)

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-crafted feature set spanning time-domain, frequency-domain, modality-specific, and cross-modal descriptors is benchmarked against end-to-end deep architectures. Extremely Randomized Trees achieves the highest macro-F1 of 0.539, outperforming the best deep model by 7.4 percentage points, with EDA spectral features emerging as the dominant discriminators. A consistent 26-point gap between pain detection (F1,=,0.815) and localization (F1,=,0.552) across all models points to a fundamental ceiling imposed by the anatomical diffuseness of peripheral autonomic pathways at 10-second resolution.