MOVEXor: Motion-Video Attention Explainer for low back pain classification

Liu, Zebang, Hicks, Yulia and Sheeran, Liba 2025. MOVEXor: Motion-Video Attention Explainer for low back pain classification. Procedia Computer Science 270 , pp. 4905-4916. 10.1016/j.procs.2025.09.617

PDF - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB)

Abstract

Accurate classification of Movement Impairment (MI) and Motor Control Impairment (MCI) in non-specific low back pain (NSLBP) is essential for targeted rehabilitation but remains challenging due to subjective assessments and subtle movement differences. We present MOVEXor, a lightweight and explainable multi-modal framework that integrates spinal curvature images and motion-derived features through a modality-aware attention gating mechanism. MOVEXor achieves high classification performance (up to 97.5% accuracy) while offering transparent decision-making via Grad-CAM and Integrated Gradients (IG). Our analysis shows that the model focuses on physiologically meaningful movement phases, particularly minimal flexion angle, and relies heavily on motion stability for classification. The fused attention-based design outperforms static fusion methods, especially when handling noisy inputs. With minimal hardware requirements and real-time explainability, MOVEXor holds strong potential as a clinical decision-support tool for both in-clinic and remote settings, enabling objective, interpretable, and personalised rehabilitation exercise of LBP subgroups.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by-nc/4.0/, Start Date: 2025-10-01
Publisher:	Elsevier
ISSN:	1877-0509
Date of First Compliant Deposit:	14 November 2025
Last Modified:	14 Nov 2025 10:00
URI:	https://orca.cardiff.ac.uk/id/eprint/182412

Actions (repository staff only)

Edit Item