Machine Learning in the Parkinson’s disease smartwatch (PADS) dataset

• Published in: NPJ Parkinson's Disease Vol. 10; no. 1; p. 9
• Main Authors: Varghese, Julian, Brenner, Alexander, Fujarski, Michael, van Alen, Catharina Marie, Plagwitz, Lucas, Warnecke, Tobias
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.01.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/53/2421; 692/617/375/346/1718; 706/648/697/129; Biomedical and Life Sciences; Biomedicine; Datasets; Machine learning; Movement disorders; Neurology; Neurosciences; Parkinson's disease; Smartphones; Smartwatches; Wearable computers
• ISSN: 2373-8057; 2373-8057
• DOI: 10.1038/s41531-023-00625-7

The utilisation of smart devices, such as smartwatches and smartphones, in the field of movement disorders research has gained significant attention. However, the absence of a comprehensive dataset with movement data and clinical annotations, encompassing a wide range of movement disorders including Parkinson’s disease (PD) and its differential diagnoses (DD), presents a significant gap. The availability of such a dataset is crucial for the development of reliable machine learning (ML) models on smart devices, enabling the detection of diseases and monitoring of treatment efficacy in a home-based setting. We conducted a three-year cross-sectional study at a large tertiary care hospital. A multi-modal smartphone app integrated electronic questionnaires and smartwatch measures during an interactive assessment designed by neurologists to provoke subtle changes in movement pathologies. We captured over 5000 clinical assessment steps from 504 participants, including PD, DD, and healthy controls (HC). After age-matching, an integrative ML approach combining classical signal processing and advanced deep learning techniques was implemented and cross-validated. The models achieved an average balanced accuracy of 91.16% in the classification PD vs. HC, while PD vs. DD scored 72.42%. The numbers suggest promising performance while distinguishing similar disorders remains challenging. The extensive annotations, including details on demographics, medical history, symptoms, and movement steps, provide a comprehensive database to ML techniques and encourage further investigations into phenotypical biomarkers related to movement disorders.