# L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection

arXiv: https://arxiv.org/abs/2406.16913 · HTML: https://mlsanity.com/paper/2406.16913/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study](https://mlsanity.com/paper/2305.11164/) (2023)
- [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)
- [Pain level and pain-related behaviour classification using GRU-based sparsely-connected RNNs](https://mlsanity.com/paper/2212.14806/) (2022)
- [Deep Learning and Computer Vision for Glaucoma Detection: A Review](https://mlsanity.com/paper/2307.16528/) (2023)
- [Learning Bodily and Temporal Attention in Protective Movement Behavior Detection](https://mlsanity.com/paper/1904.10824/) (2019)
- [ADVISE: ADaptive Feature Relevance and VISual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/2203.01289/) (2022)
- [BEE-NET: A deep neural network to identify in-the-wild Bodily Expression of Emotions](https://mlsanity.com/paper/2402.13955/) (2024)

## Built on by (most cited first, 1 of 1)

- [Context-Aware Mixture of Domain Experts for Bodily Expression of Emotion in the Wild](https://mlsanity.com/paper/2608.02331/) (2026)

## Abstract (arXiv)

Chronic Low Back Pain (CLBP) afflicts millions globally, significantly impacting individuals' well-being and imposing economic burdens on healthcare systems. While artificial intelligence (AI) and deep learning offer promising avenues for analyzing pain-related behaviors to improve rehabilitation strategies, current models, including convolutional neural networks (CNNs), recurrent neural networks, and graph-based neural networks, have limitations. These approaches often focus singularly on the temporal dimension or require complex architectures to exploit spatial interrelationships within multivariate time series data. To address these limitations, we introduce hboxL-SFAN, a lightweight CNN architecture incorporating 2D filters designed to meticulously capture the spatial-temporal interplay of data from motion capture and surface electromyography sensors. Our proposed model, enhanced with an oriented global pooling layer and multi-head self-attention mechanism, prioritizes critical features to better understand CLBP and achieves competitive classification accuracy. Experimental results on the EmoPain database demonstrate that our approach not only enhances performance metrics with significantly fewer parameters but also promotes model interpretability, offering valuable insights for clinicians in managing CLBP. This advancement underscores the potential of AI in transforming healthcare practices for chronic conditions like CLBP, providing a sophisticated framework for the nuanced analysis of complex biomedical data.