Identification Of Motor Progression In Parkinson’s Disease Using Wearable Sensors And Machine Learning

Disorder Society- Unified Parkinson’s Disease Rating Scale part III (MDS-UPDRS-III). This study aimed to establish whether a combination of wearable sensor data and machine learning algorithms with automatic feature selection can estimate the clinical rating scale and whether it is possible to monitor the motor symptom progression longitudinally, for people with PD. Results showed that the Random Forest model revealed the most accurate estimation of the MDS-UPDRS-III among the seven models and that the model estimations detected a statistically significant progression of the motor symptoms within 15 months when compared to the first visit, whereas the MDS-UPDRS-III did not capture any change.