Supplementary material for Applying Wearable Sensors and Machine Learning to the Diagnostic Challenge of Distinguishing Parkinson's Disease from Other Forms of Parkinsonism

Abstract

Parkinson's Disease (PD) and other forms of parkinsonism share motor symptoms, including tremor, bradykinesia, and rigidity. This overlap in the clinical presentation creates a diagnostic challenge, underscoring the need for objective differentiation. However, applying machine learning (ML) to clinical datasets faces challenges such as imbalanced class distributions, small sample sizes for non-PD parkinsonism, and heterogeneity within the non-PD group. This study analyzed wearable sensor data from 260 PD participants and 18 individuals with etiologically diverse forms of non-PD parkinsonism during clinical mobility tasks, using a single sensor placed on the lower-back. We evaluated the performance of ML models in distinguishing these two groups and identified the most informative mobility tasks for classification. Additionally, we examined clinical characteristics of misclassified participants and presented case studies of common challenges in clinical practice, including diagnostic uncertainty at the initial visit and changes in diagnosis over time. We also suggested potential steps to address dataset challenges which limited the models' performance. We demonstrate that ML-based analysis is a promising approach for distinguishing idiopathic PD from non-PD parkinsonism, though its accuracy remains below that of expert clinicians. Using the Timed Up and Go test as a single mobility task outperformed the use of all tasks combined, achieving a balanced accuracy of 78.2%. We also identified differences in some clinical scores between participants correctly and falsely classified by our models. These findings demonstrate the feasibility of using ML and wearable sensors for differentiating PD from other parkinsonian disorders, addressing key challenges in diagnosis, and streamlining diagnostic workflows.