Modeling and validation of wearable sensor-based gait parameters in Parkinson’s disease patients with cognitive impairment
This study highlights the potential of gait parameters derived from wearable sensors as biomarkers for cognitive impairment in PD patients. It also underscores the intricate interplay between motor and cognitive dysfunction in PD. The integration of gait analysis with machine learning models, particularly logistic regression, provides a robust, non-invasive, and scalable approach for early identification and risk stratification of cognitive decline in PD. By leveraging wearable technology, this work paves the way for innovative diagnostic strategies to enhance clinical decision-making and improve patient outcomes.
Authors
- Fengju Mao
- Kang Ren (ORCID: https://orcid.org/0000-0002-1675-0805)
- Xiaoguang Luo (ORCID: https://orcid.org/0000-0002-1804-9262)
- Zhonglue Chen (ORCID: https://orcid.org/0000-0001-7037-2481)
- Mingming Zhang (ORCID: https://orcid.org/0000-0001-7223-5528)
- Hong Guo (ORCID: https://orcid.org/0000-0001-5277-7637)
- Xiangcheng Wang (ORCID: https://orcid.org/0009-0004-2631-3563)
Institutions
- Jinan University (CN)
- Southern University of Science and Technology (CN)
- ShenZhen People’s Hospital (CN)
- Southern Medical University Shenzhen Hospital (CN)
- Shenzhen Second People's Hospital (CN)
- GCI Science & Technology (China) (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2025-07-25
- DOI
- https://doi.org/10.3389/fnagi.2025.1590224
- Citations
- 3
- Primary Topic
- Parkinson's Disease Mechanisms and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 4.17