# Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs

arXiv: https://arxiv.org/abs/2212.14806 · HTML: https://mlsanity.com/paper/2212.14806/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 70 references that are in the map.
- First posted on arXiv 20 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Multi-Label Learning with Global and Local Label Correlation](https://mlsanity.com/paper/1704.01415/) (2017)
- [GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing](https://mlsanity.com/paper/1907.04433/) (2019)
- [Understanding Autoencoders with Information Theoretic Concepts](https://mlsanity.com/paper/1804.00057/) (2018)
- [Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data](https://mlsanity.com/paper/2011.01776/) (2020)
- [A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer's disease using structural MRI images](https://mlsanity.com/paper/2203.01944/) (2022)
- [On the use of uncertainty in classifying Aedes Albopictus mosquitoes](https://mlsanity.com/paper/2110.15912/) (2021)
- [Electrical activity of fungi: Spikes detection and complexity analysis](https://mlsanity.com/paper/2008.10276/) (2020)
- [A deep convolutional neural network for classification of Aedes albopictus mosquitoes](https://mlsanity.com/paper/2110.15956/) (2021)

## Built on by (most cited first, 4 of 4)

- [BEE-NET: A deep neural network to identify in-the-wild Bodily Expression of Emotions](https://mlsanity.com/paper/2402.13955/) (2024)
- [L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection](https://mlsanity.com/paper/2406.16913/) (2024)
- [STAL: Spike Threshold Adaptive Learning Encoder for Classification of Pain-Related Biosignal Data](https://mlsanity.com/paper/2407.08362/) (2024)
- [Context-Aware Mixture of Domain Experts for Bodily Expression of Emotion in the Wild](https://mlsanity.com/paper/2608.02331/) (2026)

## Abstract (arXiv)

There is a growing body of studies on applying deep learning to biometrics analysis. Certain circumstances, however, could impair the objective measures and accuracy of the proposed biometric data analysis methods. For instance, people with chronic pain (CP) unconsciously adapt specific body movements to protect themselves from injury or additional pain. Because there is no dedicated benchmark database to analyse this correlation, we considered one of the specific circumstances that potentially influence a person's biometrics during daily activities in this study and classified pain level and pain-related behaviour in the EmoPain database. To achieve this, we proposed a sparsely-connected recurrent neural networks (s-RNNs) ensemble with the gated recurrent unit (GRU) that incorporates multiple autoencoders using a shared training framework. This architecture is fed by multidimensional data collected from inertial measurement unit (IMU) and surface electromyography (sEMG) sensors. Furthermore, to compensate for variations in the temporal dimension that may not be perfectly represented in the latent space of s-RNNs, we fused hand-crafted features derived from information-theoretic approaches with represented features in the shared hidden state. We conducted several experiments which indicate that the proposed method outperforms the state-of-the-art approaches in classifying both pain level and pain-related behaviour.