Privacy and personalisation: predicting Parkinson’s disease severity from real-world gait with federated learning
- DOI
- 10.3389/fnagi.2026.1766599
- Published
- 2026-03-09
- Container
- Frontiers in Aging Neuroscience
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/fnagi.2026.1766599,
title = {Privacy and personalisation: predicting Parkinson’s disease severity from real-world gait with federated learning},
author = {Chloe Hinchliffe and Hugo Hiden and Lisa Alcock and Rachael A. Lawson and Alison J. Yarnall and Lynn Rochester and Silvia Del Din and Paul Watson},
year = {2026},
journal = {Frontiers in Aging Neuroscience},
doi = {10.3389/fnagi.2026.1766599},
url = {https://doi.org/10.3389/fnagi.2026.1766599}
}RIS
TY - JOUR TI - Privacy and personalisation: predicting Parkinson’s disease severity from real-world gait with federated learning AU - Chloe Hinchliffe AU - Hugo Hiden AU - Lisa Alcock AU - Rachael A. Lawson AU - Alison J. Yarnall AU - Lynn Rochester AU - Silvia Del Din AU - Paul Watson PY - 2026 JO - Frontiers in Aging Neuroscience DO - 10.3389/fnagi.2026.1766599 UR - https://doi.org/10.3389/fnagi.2026.1766599 ER -
APA
Hinchliffe, C., Hiden, H., Alcock, L., Lawson, R. A., Yarnall, A. J., Rochester, L., Din, S. D., & Watson, P. (2026). Privacy and personalisation: predicting Parkinson’s disease severity from real-world gait with federated learning. Frontiers in Aging Neuroscience. https://doi.org/10.3389/fnagi.2026.1766599
Source records
- crossref · retrieved 2026-09-25T10:28:26.793Z