  For many movement disorders, such as Parkinson's disease and ataxia, disease
progression is visually assessed by a clinician using a numerical disease
rating scale. These tests are subjective, time-consuming, and must be
administered by a professional. This can be problematic where specialists are
not available, or when a patient is not consistently evaluated by the same
clinician. We present an automated method for quantifying the severity of
motion impairment in patients with ataxia, using only video recordings. We
consider videos of the finger-to-nose test, a common movement task used as part
of the assessment of ataxia progression during the course of routine clinical
checkups.
  Our method uses neural network-based pose estimation and optical flow
techniques to track the motion of the patient's hand in a video recording. We
extract features that describe qualities of the motion such as speed and
variation in performance. Using labels provided by an expert clinician, we
train a supervised learning model that predicts severity according to the Brief
Ataxia Rating Scale (BARS). The performance of our system is comparable to that
of a group of ataxia specialists in terms of mean error and correlation, and
our system's predictions were consistently within the range of inter-rater
variability. This work demonstrates the feasibility of using computer vision
and machine learning to produce consistent and clinically useful measures of
motor impairment.
