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

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).

Remote Monitoring of Treatment Response in Parkinson's Disease: The Habit of Typing on a Computer

“…We have previously shown that data collected from an in‐clinic typing task accurately differentiated early PD patients from sex‐ and age‐matched healthy controls, and replicated this result using at‐home, unsupervised data . Recently, we demonstrated similar performance with data acquired during typing on a touch‐screen smartphone .…”

On Capturing Older Adults’ Smartphone Keyboard Interaction as a Means for Behavioral Change Under Emotional Stimuli Within i-PROGNOSIS Framework

“…The latter comply with the findings of other works, such as [6], who introduced a PD motor index related computer-based keyboard interaction. However, in the case of PD, the detection of typing patterns combined with the behavioral change analysis under emotional stimuli may help to identify early onset of PD, in a more holistic and intelligent way.…”

“…Nevertheless, this approach was seldom applied to the medical field, with the rare example of [4], who used the typing speed in login sessions to evaluate sensory-motor speed in healthy subjects [4]. Moreover, Giancardo et al [5], tried to identify a pattern from keystroke dynamics that could detect a state of psychomotor impairment in healthy subjects and the ability to distinguish PD patients at the early stage of the disease from comparable healthy controls [6]. However, none of the previous approaches involved any emotional factors in their studies.…”

Discovering the Typing Behaviour of Parkinson’s Patients Using Topic Models

“…[5]. Subjects in Madrid, Spain performed typing tests by transcribing a folk tale on a word processor for 15 minutes with key stroke data being recorded.…”

“…LDA discovers semantically coherent latent topics in a collection of data (called a corpus), with low-level features (or words) being generated from a distribution of topics. Considering a recently released dataset of typing data [5], we present an approach where LDA models are trained on each of the Parkinson's and control groups. We consider a typing session to be analogous to a document, with the key hold durations corresponding to words.…”