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Less is more: Univariate modelling to detect early Parkinson’s disease from keystroke dynamics

Less is more: Univariate modelling to detect early Parkinson’s disease from keystroke dynamics
Less is more: Univariate modelling to detect early Parkinson’s disease from keystroke dynamics
We analyse keystroke hold times from typing logs to detect early signs of Parkinson’s disease. We develop a feature that captures the dynamic variation between consecutive keystrokes and demonstrate that it can be be used in a univariate model to perform classification with AUC=0.85 from only a few hundred keystrokes. This is a substantial improvement on the current baseline. We argue that previously proposed methods are based on overcomplicated models—our simpler method is not only more elegant and transparent but also more effective.
435-446
Springer
Milne, Antony
4b472700-8bf5-408a-a867-6480dc4138e9
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Nicolaou, Mihalis
9a10dc98-d381-4bc3-a864-1e4c106e6f8c
Milne, Antony
4b472700-8bf5-408a-a867-6480dc4138e9
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Nicolaou, Mihalis
9a10dc98-d381-4bc3-a864-1e4c106e6f8c

Milne, Antony, Farrahi, Katayoun and Nicolaou, Mihalis (2018) Less is more: Univariate modelling to detect early Parkinson’s disease from keystroke dynamics. In Discovery Science. Springer. pp. 435-446 . (doi:10.1007/978-3-030-01771-2_28).

Record type: Conference or Workshop Item (Paper)

Abstract

We analyse keystroke hold times from typing logs to detect early signs of Parkinson’s disease. We develop a feature that captures the dynamic variation between consecutive keystrokes and demonstrate that it can be be used in a univariate model to perform classification with AUC=0.85 from only a few hundred keystrokes. This is a substantial improvement on the current baseline. We argue that previously proposed methods are based on overcomplicated models—our simpler method is not only more elegant and transparent but also more effective.

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More information

Accepted/In Press date: 24 July 2018
e-pub ahead of print date: 7 October 2018
Published date: October 2018

Identifiers

Local EPrints ID: 425507
URI: http://eprints.soton.ac.uk/id/eprint/425507
PURE UUID: 65809502-a8e1-4532-9b45-8e955b8b811d

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Date deposited: 22 Oct 2018 16:30
Last modified: 06 Oct 2020 20:41

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Contributors

Author: Antony Milne
Author: Katayoun Farrahi
Author: Mihalis Nicolaou

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