# Multi-Representation Diagrams for Pain Recognition: Integrating Various Electrodermal Activity Signals into a Single Image

arXiv: https://arxiv.org/abs/2507.21881 · HTML: https://mlsanity.com/paper/2507.21881/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 67 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals](https://mlsanity.com/paper/2303.06845/) (2023)
- [Twins-PainViT: Towards a Modality-Agnostic Vision Transformer Framework for Multimodal Automatic Pain Assessment using Facial Videos and fNIRS](https://mlsanity.com/paper/2407.19809/) (2024)
- [Synthetic Thermal and RGB Videos for Automatic Pain Assessment utilizing a Vision-MLP Architecture](https://mlsanity.com/paper/2407.19811/) (2024)
- [A Full Transformer-based Framework for Automatic Pain Estimation using Videos](https://mlsanity.com/paper/2412.15095/) (2024)
- [PainFormer: a Vision Foundation Model for Automatic Pain Assessment](https://mlsanity.com/paper/2505.01571/) (2025)
- [Faces of Experimental Pain: Transferability of Deep Learned Heat Pain Features to Electrical Pain](https://mlsanity.com/paper/2406.11808/) (2024)
- [Transformer with Leveraged Masked Autoencoder for video-based Pain Assessment](https://mlsanity.com/paper/2409.05088/) (2024)

## Built on by (most cited first, 9 of 9)

- [A Lightweight Transformer for Pain Recognition from Brain Activity](https://mlsanity.com/paper/2604.16491/) (2026)
- [A Unified Tokenization Framework for Pain Recognition using Heterogeneous 3D Modalities](https://mlsanity.com/paper/2607.19716/) (2026)
- [ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment](https://mlsanity.com/paper/2607.19722/) (2026)
- [Towards a Unified Modality-Agnostic Multimodal Framework for Cognitive Workload Assessment](https://mlsanity.com/paper/2609.20199/) (2026)
- [Beyond the Raw Waveform: Fusing Visual Representations of EDA for Stress Detection](https://mlsanity.com/paper/2609.22095/) (2026)
- [One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment](https://mlsanity.com/paper/2605.00856/) (2026)
- [Stress Estimation in Elderly Oncology Patients Using Visual Wearable Representations and Multi-Instance Learning](https://mlsanity.com/paper/2604.06990/) (2026)
- [FUSE: Frame-Unified Stress Estimation from Facial Video](https://mlsanity.com/paper/2608.10442/) (2026)
- [UNWIND: Any-Length Facial Video for Stress Detection without Temporal Windowing](https://mlsanity.com/paper/2609.29553/) (2026)

## Abstract (arXiv)

Pain is a multifaceted phenomenon that affects a substantial portion of the population. Reliable and consistent evaluation supports individuals experiencing pain and enables the development of effective and advanced management strategies. Automatic pain-assessment systems provide continuous monitoring, guide clinical decision-making, and aim to reduce distress while preventing functional decline. Incorporating physiological signals allows these systems to deliver objective, accurate insights into an individual's condition. This study has been submitted to the Second Multimodal Sensing Grand Challenge for Next-Gen Pain Assessment (AI4PAIN). The proposed method introduces a pipeline that employs electrodermal activity signals as the input modality. Multiple signal representations are generated and visualized as waveforms, which are then jointly presented within a unified multi-representation diagram. Extensive experiments using diverse processing and filtering techniques, along with various representation combinations, highlight the effectiveness of the approach. It consistently achieves comparable and, in several cases, superior results to traditional fusion methods, positioning it as a robust alternative for integrating different signal representations or modalities.