Context-agnostic machine learning for Parkinson's disease motor symptom detection using wearable sensors

Parkinson's disease is a rapidly growing neurodegenerative disorder with various motor and non-motor symptoms, affecting millions of people worldwide. These symptoms demonstrate significant medication-related fluctuations and inter-patient variability, highlighting the need for personalized management. Objective longitudinal symptom monitoring through wearable sensors and machine learning can support individualized care. However, to date, most approaches have been tested in lab-constrained environments. This study aims to develop a modular pipeline to automatically detect three cardinal Parkinson's disease motor symptoms, tremor, bradykinesia, and levodopa-induced dyskinesia in more realistic scenarios.

The proposed approach was evaluated on three datasets: the Levodopa Response Study and two newly introduced ALAMEDA datasets, containing tri-axial wrist accelerometer data collected with commercial wearable devices during clinical assessments and activities of daily living. For each symptom, separate context-agnostic models were developed using 92 hand-crafted features. Multiple segmentation window lengths and preprocessing techniques, including resampling and dimensionality reduction, alongside various machine learning models, including logistic regression, k-nearest neighbor, multilayer perceptron, support vector machine, decision tree, AdaBoost, and random forest, were explored. Statistical significance between configurations was assessed with the Wilcoxon signed-rank test. Model interpretability was investigated using Shapley additive explanations to identify highly influential predictors and assess their physiological relevance.

In the Levodopa Response Study dataset, tremor, bradykinesia, and dyskinesia detection reached 0.664, 0.636, and 0.443 area under the precision-recall curve, respectively, demonstrating scalability in high-complexity settings and revealing physiologically meaningful patterns. When evaluated on the ALAMEDA datasets, tremor and dyskinesia detection achieved 0.879 and 0.648 area under the precision-recall curve, highlighting strong model and feature generalizability. Across symptoms, longer segmentation windows and random forest classifiers performed better, while synthetic oversampling and principal component analysis showed limited impact.

Automated Parkinson's disease symptom detection is feasible in more realistic, free-living conditions, with only a slight performance decrease despite substantially increased complexity. With carefully selected features and pipeline components, the objective, unobtrusive monitoring of motor symptoms can support personalized, evidence-based treatment suggestions, eventually improving patients' quality of life.

Not applicable.