Less is More: Univariate Modelling to Detect Early Parkinson’s Disease from Keystroke Dynamics

  • Antony MilneEmail author
  • Katayoun Farrahi
  • Mihalis A. Nicolaou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11198)


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 \(\text {AUC}=0.85\) 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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Antony Milne
    • 1
    Email author
  • Katayoun Farrahi
    • 2
  • Mihalis A. Nicolaou
    • 1
    • 3
  1. 1.Department of ComputingGoldsmiths, University of LondonLondonUK
  2. 2.Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK
  3. 3.The Cyprus InstituteNicosiaCyprus

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