[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123516-en":3,"doc-seo-123516-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123516,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",7,"Healthcare","PDWearML - Leveraging Daily Activities for Fast Parkinson’s Disease Severity Assessment with Wearable Machine Learning","Objective: Achieving effective and robust free-living Parkinson’s disease (PD) severity assessment with wearable intelligence depends on selecting clinically relevant features, representative activities, and suitable machine learning algorithms. Methods: PDWearML is a unified analytic framework that optimizes wearable ML using simple daily activities by combining annotation criteria, feature-importance analysis, representative activity selection, and PD severity assessment. A 12-month supervised study used 100 PD patients and 35 controls with Huawei smartwatches and Shimmer, with PD severity rated by physicians using the Hoehn and Yahr scale.","[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk](https://eprints.whiterose.ac.uk)  \nUniversities of Leeds, Sheffield and York  \nDeposited via The University of Sheffield.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/id/eprint/235850/](https://eprints.whiterose.ac.uk/id/eprint/235850/)  \n[Version: Accepted Version](Version: Accepted Version)  \nArticle:  \nWang, X. , Peng, X. , Xu, Z. et al. (2025) PDWearML: Leveraging daily activities for fast Parkinson’s disease severity assessment with wearable machine learning. IEEE Transactions on Biomedical Engineering. ISSN: 0018-9294  \n[https://doi.org/10.1109/TBME.2025.3648564](https://doi.org/10.1109/TBME.2025.3648564)  \n© 2025 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in IEEE Transactions on Biomedical Engineering is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \nGENERIC COLORIZED JOURNAL, VOL. XX, NO. XX, XXXX 2023 1  \nPDWearML: Leveraging Daily Activities for Fast Parkinson’s Disease Severity Assessment with Wearable Machine Learning  \nXulong Wang, Xiyang Peng, Zheyuan Xu, Mingchang Xu, Yun Yang, Menghui Zhou, Zhong Zhao, Peng  \nYue and Po Yang Senior Member, IEEE  \nAbstractÐObjective: Achieving effective and robust free-living PD severity assessment with wearable intelligence technologies requires a deep understanding of clinically relevant features, representative activities, and machine learning algorithms. Methods: We designed a unified analytic framework (PDWearML) to optimise wearable ML approaches with simple daily activities for fast assessment of PD severity. It comprises annotation criteria, feature importance analysis, representative activity combination, and PD severity assessment. We conducted a 12-month study, developing a supervised PD wearable dataset containing 100 PD patients and 35 age-matched healthy controls using Huawei smartwatches and Shimmer. PD severity, assessed by trained physicians using the Hoehn and Yahr (H&Y) scale. Results: The results reveal that through optimising multi-level feature extraction and combining three representative daily activities (WALK, ARISING-FROM-CHAIR, and DRINK), our smartwatch-based machine learning approach can assess PD severity in supervised settings within 2 minutes with an accuracy of up to 84.7%. Significance: This work holds significant clinical value, offering a potential auxiliary tool for faster, more tailored interventions in PD healthcare. Code is available at code ocean platform and [https://github.com/wang-xulong/PDWearML](https://github.com/wang-xulong/PDWearML).  \nIndex TermsÐParkinson’s disease, fast assessment, subject adherence, wearable intelligence, activities of daily living  \nI. INTRODUCTION  \nWITH notable advancement of machine learning (ML)  \ntechniques, wearable intelligence (WI) has made significant strides in developing intelli","cbCaim1y6RlKJ88c","https://ap.wps.com/l/cbCaim1y6RlKJ88c","pdf",15691194,1,14,"English","en",105,"# Abstract\n## Objective, Methods, and Study Design\n## Results and Significance\n# Index Terms\n## Parkinson’s disease and Wearable Intelligence","[{\"question\":\"What problem does PDWearML target in PD management?\",\"answer\":\"PDWearML targets the need for effective and robust free-living PD severity assessment using wearable intelligence, emphasizing clinically relevant features, representative activities, and appropriate machine learning methods.\"},{\"question\":\"How does PDWearML perform severity assessment?\",\"answer\":\"It uses a unified analytic framework that includes annotation criteria, feature-importance analysis, selection and combination of representative daily activities, and a final PD severity assessment model.\"},{\"question\":\"What data and devices were used in the 12-month study?\",\"answer\":\"The study used wearable data collected over 12 months from 100 PD patients and 35 age-matched healthy controls using Huawei smartwatches and Shimmer.\"}]","PDWearML - 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