Using machine learning to identify Parkinson’s disease severity subtypes with multimodal data
Digital biomarkers obtained from wearable sensors attached bilaterally to body segments demonstrate potential for classifying PD severity subtypes and tracking disease progression. Our findings emphasized the clinical value of sensor-based gait analysis in PD management, which suggested its integration into personalized monitoring systems and therapeutic interventions for persons with PD.
Authors
- Changhong Youm (ORCID: https://orcid.org/0000-0001-7574-5259)
- Sang‐Myung Cheon (ORCID: https://orcid.org/0000-0001-5905-2188)
- Hwayoung Park (ORCID: https://orcid.org/0000-0002-4625-9021)
- Hyejin Choi (ORCID: https://orcid.org/0000-0001-6323-1453)
- Minsoo Kim (ORCID: https://orcid.org/0000-0001-6813-7335)
- Bohyun Kim (ORCID: https://orcid.org/0000-0003-0012-5615)
- Juseon Hwang
Institutions
- Dong-A University (KR)
Publication Details
- Journal
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2025-06-02
- DOI
- https://doi.org/10.1186/s12984-025-01648-2
- Citations
- 9
- Primary Topic
- Parkinson's Disease Mechanisms and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 12.50
Funders
- National Research Foundation
- Dong-A University
- Ministry of Education
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea