KeyGAN: Synthetic keystroke data generation in the context of digital phenotyping
KeyGAN presents strong potential as a realistic keystroke data synthesizer, displaying impressive capability to reproduce complex typing patterns relevant to biomarkers for neurological disorders, like Parkinson's Disease. The ability of its synthetic data to effectively supplement real data for training algorithms without affecting performance implies significant promise for advancing research in digital biomarkers for neurodegenerative and psychomotor disorders.
Authors
- Luca Giancardo (ORCID: https://orcid.org/0000-0002-4862-2277)
- Teresa Arroyo‐Gallego (ORCID: https://orcid.org/0000-0003-2874-0230)
- Rubén Vera-Rodríguez (ORCID: https://orcid.org/0000-0002-6338-8511)
- Ashley A Holmes (ORCID: https://orcid.org/0000-0002-9029-9418)
- Julián Fiérrez (ORCID: https://orcid.org/0000-0002-6343-5656)
- Aythami Morales (ORCID: https://orcid.org/0000-0002-7268-4785)
- Alejandro Acien
Institutions
- GVD Corporation (United States) (US)
- Winston-Salem/Forsyth County Schools (US)
- The University of Texas Health Science Center (US)
- Universidad Autónoma de Madrid (ES)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2024-11-29
- DOI
- https://doi.org/10.1016/j.compbiomed.2024.109460
- Primary Topic
- User Authentication and Security Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00