Parkinson’s disease (PD) is a progressive neurodegenerative disorder whose prevalence has
steadily been rising over the years. Specialist neurologists across the world assess and diagnose patients
with PD, although the diagnostic process is time-consuming and various symptoms take years to appear,
which means that the diagnosis is prone to human error. The partial automatization of PD assessment
and diagnosis through computational processes has therefore been considered for some time. One
well-known tool for PD assessment is finger tapping (FT), which can now be assessed through computer
vision (CV). Artificial intelligence and related advances over recent decades, more specifically in the
area of CV, have made it possible to develop computer systems that can help specialists assess and
diagnose PD. The aim of this study is to review some advances related to CV techniques and FT so as
to offer insight into future research lines that technological advances are now opening up.
