# Multi-task Neural Networks for Pain Intensity Estimation using Electrocardiogram and Demographic Factors

arXiv: https://arxiv.org/abs/2407.19475 · HTML: https://mlsanity.com/paper/2407.19475/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 28 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Multi-task Neural Networks for Personalized Pain Recognition from Physiological Signals](https://mlsanity.com/paper/1708.08755/) (2017)
- [Physiological and behavioral profiling for nociceptive pain estimation using personalized multitask learning](https://mlsanity.com/paper/1711.04036/) (2017)

## Built on by (most cited first, 7 of 7)

- [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)
- [PainFormer: a Vision Foundation Model for Automatic Pain Assessment](https://mlsanity.com/paper/2505.01571/) (2025)
- [A Lightweight Transformer for Pain Recognition from Brain Activity](https://mlsanity.com/paper/2604.16491/) (2026)
- [ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment](https://mlsanity.com/paper/2607.19722/) (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)

## Abstract (arXiv)

Pain is a complex phenomenon which is manifested and expressed by patients in various forms. The immediate and objective recognition of it is a great of importance in order to attain a reliable and unbiased healthcare system. In this work, we elaborate electrocardiography signals revealing the existence of variations in pain perception among different demographic groups. We exploit this insight by introducing a novel multi-task neural network for automatic pain estimation utilizing the age and the gender information of each individual, and show its advantages compared to other approaches.