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

TL;DR

This study demonstrates that analyzing key hold times during routine computer keyboard use can effectively detect early Parkinson's disease motor signs, offering a non-intrusive, accessible monitoring tool with high accuracy.

Contribution

The paper introduces a novel method that uses machine learning to analyze keyboard interaction patterns for early PD detection without additional hardware.

Findings

Key hold time analysis discriminates early PD with AUC = 0.81

Performance is comparable or superior to clinical finger tapping tests

Method enables non-intrusive, routine monitoring of motor signs

Abstract

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).