# Exploring Remote Photoplethysmography for Neonatal Pain Detection from Facial Videos

arXiv: https://arxiv.org/abs/2604.25680 · HTML: https://mlsanity.com/paper/2604.25680/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 82 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science](https://mlsanity.com/paper/1603.06212/) (2016)
- [Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals](https://mlsanity.com/paper/2303.06845/) (2023)
- [Multimodal Spatio-Temporal Deep Learning Approach for Neonatal Postoperative Pain Assessment](https://mlsanity.com/paper/2012.02175/) (2020)
- [Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition](https://mlsanity.com/paper/1803.04567/) (2018)
- [Multi-Channel Neural Network for Assessing Neonatal Pain from Videos](https://mlsanity.com/paper/1908.09254/) (2019)
- [Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction](https://mlsanity.com/paper/2105.08822/) (2021)
- [Neonatal Face and Facial Landmark Detection from Video Recordings](https://mlsanity.com/paper/2302.04341/) (2023)
- [Neonatal Pain Expression Recognition Using Transfer Learning](https://mlsanity.com/paper/1807.01631/) (2018)
- [Harnessing the Power of Deep Learning Methods in Healthcare: Neonatal Pain Assessment from Crying Sound](https://mlsanity.com/paper/1909.02543/) (2019)
- [First Investigation Into the Use of Deep Learning for Continuous Assessment of Neonatal Postoperative Pain](https://mlsanity.com/paper/2003.10601/) (2020)
- [Deep learning based non-contact physiological monitoring in Neonatal Intensive Care Unit](https://mlsanity.com/paper/2207.11886/) (2022)
- [MOMBAT: Heart Rate Monitoring from Face Video using Pulse Modeling and Bayesian Tracking](https://mlsanity.com/paper/2005.04618/) (2020)
- [AuE-IPA: An AU Engagement Based Infant Pain Assessment Method](https://mlsanity.com/paper/2212.04764/) (2022)

## Abstract (arXiv)

Unaddressed pain in neonates can lead to adverse effects, including delayed development and slower weight gain, emphasising the need for more objective and reliable pain assessment methods. Hence, automated methods using behavioural and physiological pain indicators have been developed to aid healthcare professionals in the Neonatal ICU. Traditional contact-based methods for physiological parameter estimation are unsuitable for long-term monitoring and increase the risk of spreading diseases like COVID-19. We introduce a novel approach using remote photoplethysmography (rPPG) to estimate pulse signals in a non-contact manner and employ them for neonatal pain detection. The temporal signals acquired from regions-of-interest (ROIs) affected by skin deformations may exhibit lower quality and provide erroneous rPPG signals. Therefore, we incorporated a quality parameter to select the temporal signals obtained from ROIs that are least affected by skin deformations. Further, we employed signal-to-noise ratio as a fitness parameter to extract the rPPG signal corresponding to the clip that is least affected by noise. Experimental findings demonstrate that the rPPG signals provide useful information for neonatal pain detection, and signals extracted from the blue colour channel outperform those extracted from other colour channels. We also show that combining rPPG and audio features provides better results than individual modalities.