# Computer keyboard interaction as an indicator of early Parkinson's disease

arXiv: https://arxiv.org/abs/1604.08620 · HTML: https://mlsanity.com/paper/1604.08620/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2016.

## Built on by (most cited first, 9 of 9)

- [TypeNet: Deep Learning Keystroke Biometrics](https://mlsanity.com/paper/2101.05570/) (2021)
- [Online handwriting, signature and touch dynamics: tasks and potential applications in the field of security and health](https://mlsanity.com/paper/2202.12693/) (2022)
- [A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities](https://mlsanity.com/paper/2505.00525/) (2025)
- [Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A survey](https://mlsanity.com/paper/2502.10703/) (2025)
- [Cross-dataset Multivariate Time-series Model for Parkinson's Diagnosis via Keyboard Dynamics](https://mlsanity.com/paper/2510.15950/) (2025)
- [Detecting Cognitive Signatures in Typing Behavior for Non-Intrusive Authorship Verification](https://mlsanity.com/paper/2603.00177/) (2026)
- [Leveraging Unlabelled Data in Multiple-Instance Learning Problems for Improved Detection of Parkinsonian Tremor in Free-Living Conditions](https://mlsanity.com/paper/2305.00249/) (2023)
- [Inverse Reinforcement Learning for Interpretable Keystroke Biomarkers in Parkinson's Disease](https://mlsanity.com/paper/2606.25270/) (2026)
- [Backspace as a Natural Experiment: An Accelerated Failure Time Model of Selective Post-Error Motor Impairment in Parkinsons Disease](https://mlsanity.com/paper/2607.24796/) (2026)

## 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).