Computer keyboard interaction as an indicator of early Parkinson's disease
Not on the map. Why?
Abstract (arXiv)
Parkinson's disease (PD) is a slowly progressing neurodegenerative disease with early manifestation of motor signs. Objective measurements of motor signs are of vital importance for diagnosing, monitoring and developing disease modifying therapies, particularly for the early stages of the disease when putative neuroprotective treatments could stop neurodegeneration. Current medical practice has limited tools to routinely monitor PD motor signs with enough frequency and without undue burden for patients and the healthcare system. In this paper, we present data indicating that the routine interaction with computer keyboards can be used to detect motor signs in the early stages of PD. We explore a solution that measures the key hold times (the time required to press and release a key) during the normal use of a computer without any change in hardware and converts it to a PD motor index. This is achieved by the automatic discovery of patterns in the time series of key hold times using an ensemble regression algorithm. This new approach discriminated early PD groups from controls with an AUC = 0.81 (n = 42/43; mean age = 59.0/60.1; women = 43%/60%;PD/controls). The performance was comparable or better than two other quantitative motor performance tests used clinically: alternating finger tapping (AUC = 0.75) and single key tapping (AUC = 0.61).
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Apr 2016.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 0; 2025: 3; 2026: 3.
9 papers.
- 2021TypeNet: Deep Learning Keystroke Biometricscited by 19
- 2022Online handwriting, signature and touch dynamics: tasks and potential applications in the field of security and healthcited by 7
- 2025A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalitiescited by 4
- 2025Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A surveycited by 2
- 2025Cross-dataset Multivariate Time-series Model for Parkinson's Diagnosis via Keyboard Dynamicscited by 1
- 2023Leveraging Unlabelled Data in Multiple-Instance Learning Problems for Improved Detection of Parkinsonian Tremor in Free-Living Conditionscited by 0
- 2026Detecting Cognitive Signatures in Typing Behavior for Non-Intrusive Authorship Verificationcited by 0
- 2026Inverse Reinforcement Learning for Interpretable Keystroke Biomarkers in Parkinson's Diseasecited by 0
- 2026Backspace as a Natural Experiment: An Accelerated Failure Time Model of Selective Post-Error Motor Impairment in Parkinsons Diseasecited by 0
- 2026Backspace as a Natural Experiment: An Accelerated Failure Time Model of Selective Post-Error Motor Impairment in Parkinsons Disease30 Jun 2026
- 2026Inverse Reinforcement Learning for Interpretable Keystroke Biomarkers in Parkinson's Disease24 Jun 2026
- 2026Detecting Cognitive Signatures in Typing Behavior for Non-Intrusive Authorship Verification26 Feb 2026
- 2025Cross-dataset Multivariate Time-series Model for Parkinson's Diagnosis via Keyboard Dynamics10 Oct 2025
- 2025A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities1 May 2025
- 2025Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A survey15 Feb 2025
- 2023Leveraging Unlabelled Data in Multiple-Instance Learning Problems for Improved Detection of Parkinsonian Tremor in Free-Living Conditions29 Apr 2023
- 2022Online handwriting, signature and touch dynamics: tasks and potential applications in the field of security and health24 Feb 2022
- 2021TypeNet: Deep Learning Keystroke Biometrics14 Jan 2021