Leveraging machine learning for digital gait analysis in ataxia using sensor-free motion capture

Gait disturbances are the clinical hallmark of ataxia. Their severity is assessed within a well-established clinical scale, which only allows coarse scoring and does not reflect the complexity of individual gait deterioration. We investigated whether sensor-free motion capture enables to replicate clinical scoring and improve the assessment of gait disturbances.

The normal walking task during clinical assessment was videotaped in 91 ataxia patients and 28 healthy controls. A full-body pose estimation model (AlphaPose) was used to extract positions, distances, and angles over time while walking. The resulting time series were analyzed with four machine learning (ML) models, which were combinations of feature extraction (tsfresh, ROCKET) and prediction methods (XGBoost, Ridge). First, in a regression and classification approach, we trained the ML models on reconstructing the clinical score. Second, we used explainable AI (SHAP) to identify the most important time series. Third, we investigated time series features to study longitudinal changes.

Gait disturbances are assessed with high accuracy by ML models, slightly improving human rating (i) in the categorial prediction of the clinical score (F1-score best model: 63.99%, human: 60.57% F1-score), (ii) in the detection of subtle changes (pre-symptomatic patients, clinically rated unimpaired are differentiated from HC with a F1-score of 75.96%) and (iii) in the detection of longitudinal changes over time (Pearson's correlation coefficient model: -0.626, p < 0.01; human: -0.060, not significant).

ML-based analysis shows improved sensitivity in assessing gait disturbances in ataxia. Subtle and longitudinal changes can be captured within this study. These findings suggest that such approaches may hold promise as potential outcome parameters for early interventions, therapy monitoring, and home-based assessments.