  Tremor is a key diagnostic feature of Parkinson's Disease (PD), Essential
Tremor (ET), and other central nervous system (CNS) disorders. Clinicians or
trained raters assess tremor severity with TETRAS scores by observing patients.
Lacking quantitative measures, inter- or intra- observer variabilities are
almost inevitable as the distinction between adjacent tremor scores is subtle.
Moreover, clinician assessments also require patient visits, which limits the
frequency of disease progress evaluation. Therefore it is beneficial to develop
an automated assessment that can be performed remotely and repeatably at
patients' convenience for continuous monitoring. In this work, we proposed to
train a deep neural network (DNN) with rank-consistent ordinal regression using
276 clinical videos from 36 essential tremor patients. The videos are coupled
with clinician assessed TETRAS scores, which are used as ground truth labels to
train the DNN. To tackle the challenge of limited training data, optical flows
are used to eliminate irrelevant background and statistic objects from RGB
frames. In addition to optical flows, transfer learning is also applied to
leverage pre-trained network weights from a related task of tremor frequency
estimate. The approach was evaluated by splitting the clinical videos into
training (67%) and testing sets (0.33%). The mean absolute error on TETRAS
score of the testing results is 0.45, indicating that most of the errors were
from the mismatch of adjacent labels, which is expected and acceptable. The
model predications also agree well with clinical ratings. This model is further
applied to smart phone videos collected from a PD patient who has an implanted
device to turn "On" or "Off" tremor. The model outputs were consistent with the
patient tremor states. The results demonstrate that our trained model can be
used as a means to assess and track tremor severity.
