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

Rana M Khalil; Lisa M Shulman; Ann L Gruber-Baldini; Stephen G Reich; Joseph M Savitt; Jeffrey M Hausdorff; Rainer von Coelln; Michael P Cummings

doi:10.3390/biomedicines13030572

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

Biomedicines. 2025 Feb 25;13(3):572. doi: 10.3390/biomedicines13030572.

Authors

Affiliations

¹ Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.
² Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
³ Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
⁴ Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv 6492416, Israel.
⁵ Department of Physical Therapy, Faculty of Medicine & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
⁶ Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel.
⁷ Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA.
⁸ Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA.

Abstract

Background/Objectives: Parkinson's Disease (PD) and other forms of parkinsonism share motor symptoms, including tremor, bradykinesia, and rigidity. The overlap in their clinical presentation creates a diagnostic challenge, as conventional methods rely heavily on clinical expertise, which can be subjective and inconsistent. This highlights the need for objective, data-driven approaches such as machine learning (ML) in this area. However, applying 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. Methods: This study analyzed wearable sensor data from 260 PD participants and 18 individuals with etiologically diverse forms of non-PD parkinsonism, which were collected 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 the clinical characteristics of misclassified participants and presented case studies of common challenges in clinical practice, including diagnostic uncertainty at the patient's initial visit and changes in diagnosis over time. We also suggested potential steps to address the dataset challenges which limited the models' performance. Results: Feature importance analysis revealed the Timed Up and Go (TUG) task as the most informative for classification. When using the TUG test alone, the models' performance exceeded that of combining all tasks, achieving a balanced accuracy of 78.2%, which is within 0.2% of the balanced diagnostic accuracy of movement disorder experts. We also identified differences in some clinical scores between the participants correctly and falsely classified by our models. Conclusions: These findings demonstrate the feasibility of using ML and wearable sensors for differentiating PD from other parkinsonian disorders, addressing key challenges in its diagnosis and streamlining diagnostic workflows.

Keywords: Parkinson’s disease; diagnosis; machine learning; mobility testing; neurodegenerative diseases; parkinsonism; timed up and go test; wearable sensors.

Abstract

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