  Bradykinesia, characterized by involuntary slowing or decrement of movement,
is a fundamental symptom of Parkinson's Disease (PD) and is vital for its
clinical diagnosis. Despite various methodologies explored to quantify
bradykinesia, computer vision-based approaches have shown promising results.
However, these methods often fall short in adequately addressing key
bradykinesia characteristics in repetitive limb movements: "occasional arrest"
and "decrement in amplitude."
  This research advances vision-based quantification of bradykinesia by
introducing nuanced numerical analysis to capture decrement in amplitudes and
employing a simple deep learning technique, LSTM-FCN, for precise
classification of occasional arrests. Our approach structures the
classification process hierarchically, tailoring it to the unique dynamics of
bradykinesia in PD.
  Statistical analysis of the extracted features, including those representing
arrest and fatigue, has demonstrated their statistical significance in most
cases. This finding underscores the importance of considering "occasional
arrest" and "decrement in amplitude" in bradykinesia quantification of limb
movement. Our enhanced diagnostic tool has been rigorously tested on an
extensive dataset comprising 1396 motion videos from 310 PD patients, achieving
an accuracy of 80.3%. The results confirm the robustness and reliability of our
method.
