# Multi-Channel Neural Network for Assessing Neonatal Pain from Videos

arXiv: https://arxiv.org/abs/1908.09254 · HTML: https://mlsanity.com/paper/1908.09254/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [Unobtrusive Pain Monitoring in Older Adults with Dementia using Pairwise and Contrastive Training](https://mlsanity.com/paper/2101.03251/) (2021)
- [Intelligent Sight and Sound: A Chronic Cancer Pain Dataset](https://mlsanity.com/paper/2204.04214/) (2022)
- [Robust Neonatal Face Detection in Real-world Clinical Settings](https://mlsanity.com/paper/2204.00655/) (2022)
- [Pattern Recognition in Vital Signs Using Spectrograms](https://mlsanity.com/paper/2108.03168/) (2021)
- [Exploring Remote Photoplethysmography for Neonatal Pain Detection from Facial Videos](https://mlsanity.com/paper/2604.25680/) (2026)

## Abstract (arXiv)

Neonates do not have the ability to either articulate pain or communicate it non-verbally by pointing. The current clinical standard for assessing neonatal pain is intermittent and highly subjective. This discontinuity and subjectivity can lead to inconsistent assessment, and therefore, inadequate treatment. In this paper, we propose a multi-channel deep learning framework for assessing neonatal pain from videos. The proposed framework integrates information from two pain indicators or channels, namely facial expression and body movement, using convolutional neural network (CNN). It also integrates temporal information using a recurrent neural network (LSTM). The experimental results prove the efficiency and superiority of the proposed temporal and multi-channel framework as compared to existing similar methods.