A hallmark of cerebellar disease is impaired accuracy
of intended movement which is often summarized as ataxia
or incoordination. The diagnosis and assessment of cerebellar
ataxia (CA) is primarily based on the expert clinician’s visual
and auditory observations of the performance of these tasks,
and as such, a significant level of subjectivity is implied. In
order to address the limitations of this subjectivity we designed
a novel automated system, utilizing the Microsoft Kinect device,
to capture the finger chase task (in the assessment of upper
limb ataxia) which is a part of the assessment of cerebellar
upper limb function. Capturing the movements of the marker
attached on the subject’s finger when following the target point
generated by the program that mimics the finger movement
of the clinician, we were able to capture the disability and
provide a novel objective measure of the CA affecting upper
limb function. In our approach, we essentially quantified the
difference between the intended and achieved trajectories using
Dynamic Time Warping (DTW) technique. Further, signal delay
times and directional changes of the velocity of the marker were
considered in characterizing the disability associated with patient’s
finger movements. Finally, Principal Component Analysis
(PCA) was employed to combine all the relevant features, reduce
feature dimension while enhancing the robustness. This analysis
demonstrates a significant separation between normal subjects
and CA patients, highlighting this approach as a potential
diagnostic aid in the objective assessment of Cerebellar ataxia.