Finger drawing on smartphone screens enables early Parkinson’s disease detection through hybrid 1D-CNN and BiGRU deep learning architecture
This study establishes the technical feasibility of a hybrid deep learning framework for early PD detection using smartphone-captured finger motion dynamics. The developed model effectively combines one-dimensional convolutional neural networks with bidirectional GRUs to analyze drawing tasks. Distinct from existing approaches that rely on clinical rating scales, neuroimaging modalities, or stylus-based digital assessments, this telemedicine-compatible method requires only bare-finger interactions on consumer-grade smartphones and enables operator-independent assessments. Furthermore, it facilitates cost-effective and convenient PD assessment in remote healthcare and patient monitoring, particularly in resource-limited settings.
Authors
- Jiajun Sun (ORCID: https://orcid.org/0000-0002-6020-4457)
- Zhigeng Pan (ORCID: https://orcid.org/0000-0003-0717-5850)
- Pengfei Leng
- Erxi Wu (ORCID: https://orcid.org/0000-0002-1680-3639)
- Zhaohui Zhu (ORCID: https://orcid.org/0000-0001-8494-0152)
- Mingming Ma (ORCID: https://orcid.org/0000-0002-1365-3559)
Institutions
- Hangzhou Normal University (CN)
- Nanjing University of Information Science and Technology (CN)
- Nanjing Institute of Technology (CN)
- Hangzhou Vocational and Technical College (CN)
- Huaiyin Normal University (CN)
- Hangzhou Red Cross Hospital (CN)
- Zhejiang University of Water Resource and Electric Power (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-07-14
- DOI
- https://doi.org/10.1371/journal.pone.0327733
- Citations
- 4
- Primary Topic
- Parkinson's Disease Mechanisms and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 5.56
Funders
- Humanities and Social Science Fund of Ministry of Education of China
- Natural Science Foundation of Zhejiang Province