Multidimensional abnormal gait analysis and biomarker identification for patients with spinocerebellar ataxia type 3 using an Azure Kinect-based motion capture system.

BACKGROUND

Spinocerebellar Ataxia Type 3 (SCA3), the most common hereditary ataxia in China, is characterized by progressive gait dysfunction. While quantitative gait analysis provides critical insights into movement disorder management, conventional motion capture systems are often cost-prohibitive and impractical for clinical use.

OBJECTIVES

We propose using the markerless Azure Kinect, a cost-effective and portable tool for gait analysis, to detect SCA3-specific gait patterns and identify gait parameters associated with disease severity and duration.

METHODS

We enrolled 38 patients with SCA3 patients and 42 healthy controls (HCs). Gait was recorded using an Azure Kinect. Multiple gait parameters were computed and compared with t-tests/Mann-Whitney U tests. The receiver operating characteristic (ROC) analysis identified discriminatory biomarkers, while Pearson's test assessed gait-clinical characteristic associations.

RESULTS

Patients with SCA3 exhibited increased mediolateral margins of stability (MOS, p < 0.01), wider step width (p < 0.001), shorter stride length (p = 0.003), slower gait speed (p = 0.007), and reduced hip/knee/ankle joint angles (p < 0.05) compared to HCs. Step width demonstrated the highest diagnostic accuracy (AUC = 0.878, cutoff = 0.197). Increased medial-lateral MOS was negatively correlated with step length (r = -0.52∼-0.45, P < 0.005). Minimal hip frontal angles negatively correlated with SARA scores (r = -0.46, p = 0.004) and disease duration (r = -0.35, p = 0.028), reflecting a progressive cerebellar degeneration.

CONCLUSION

In SCA3, gait abnormalities such as increased step width and shortened stride length indicate compensatory adaptations exist to enhance dynamic stability. Step width is identified as a sensitive biomarker for SCA3 screening.