  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).
