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

TL;DR

L-SFAN is a lightweight neural network that effectively captures spatial-temporal features from biomedical data to improve pain behavior detection in CLBP patients, with enhanced accuracy and interpretability.

Contribution

The paper introduces a novel lightweight CNN architecture with spatial-temporal focus, multi-head self-attention, and improved interpretability for pain behavior detection.

Findings

Achieves competitive accuracy with fewer parameters.

Enhances model interpretability for clinical insights.

Demonstrates effectiveness on EmoPain database.

Abstract

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 \hbox{L-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.