Sensor-free motion registration and automated movement evaluation: Leveraging machine learning for clinical gait analysis in ataxia disorders

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 (Pearsons 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 (Pearsons 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.