Gait disturbances are the clinical hallmark of ataxia disorders, fundamentally impairing the mobility of ataxia patients. In clinical routine and research the severity of the gait disturbances is assessed within a well-established clinical scale and graded into categorial levels. Sensor-free motion registration and subsequent movement analysis allowed to overcome the obvious shortcoming of such coarse grading: Using time series models (tsfresh, ROCKET) we were not only able to successfully reproduce the categorial scaling (Human performance: 44.88%F1-score; our model: 80.28%F1-score). Particularly subtle, early gait disturbances and longitudinal progression below the perception threshold of the human examiner could be captured (Pearson’s correlation coefficient human performance -0.060, not significant; our model: -0.626,p< 0.01). Furthermore, SHAP analysis allowed to identify the most important features for each clinical level of gait deterioration. This could further improve the sensitivity to capture longitudinal changes tailored to the pre-existing level of gait disturbances (Pearson’s correlation coefficients up to -0.988,p< 0.01). In conclusion, the ML-based analysis could significantly improve the sensitivity in the assessment of gait disturbances in ataxia patients. Thus, it qualifies as a potential digital outcome parameter for early interventions, therapy monitoring, and home recordings.