Essential Tremor Severity Assessment Using Handwriting Analysis and Machine Learning
These results underscore the machine learning-based pipeline's potential as a non-invasive and trustworthy diagnostic tool for clinical use and telemedicine applications. Moreover, the combination of geometric features, FMT-TRS scores, clinically oriented evaluation metrics, and classical statistical and machine learning models offers a robust, interpretable, explainable, and clinically meaningful analytical framework.
Authors
- Alberto Bergareche (ORCID: https://orcid.org/0000-0002-6055-1061)
- Esther Fernández (ORCID: https://orcid.org/0000-0002-9408-7076)
- Karmele López-de-Ipiña
- Jaime Méndez
Institutions
- University of the Basque Country (ES)
- University of Cambridge (GB)
- Biogipuzkoa Health Research Institute (ES)
- NuVue Therapeutics (United States) (US)
Publication Details
- Journal
- Sensors
- Published
- 2025-12-31
- DOI
- https://doi.org/10.3390/s26010244
- Primary Topic
- Neurological disorders and treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Eusko Jaurlaritza
- Euskal Herriko Unibertsitatea