MOVEXor: motion-video attention explainer for low back pain classification

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.

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ORCID for Liba Sheeran: orcid.org/0000-0002-1502-764X

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