  Objective The coordination of human movement directly reflects function of
the central nervous system. Small deficits in movement are often the first sign
of an underlying neurological problem. The objective of this research is to
develop a new end-to-end, deep learning-based system, Rapid-Motion-Track (RMT)
that can track the fastest human movement accurately when webcams or laptop
cameras are used.
  Materials and Methods We applied RMT to finger tapping, a well-validated test
of motor control that is one of the most challenging human motions to track
with computer vision due to the small keypoints of digits and the high
velocities that are generated. We recorded 160 finger tapping assessments
simultaneously with a standard 2D laptop camera (30 frames/sec) and a
high-speed wearable sensor-based 3D motion tracking system (250 frames/sec).
RMT and a range of DLC models were applied to the video data with tapping
frequencies up to 8Hz to extract movement features.
  Results The movement features (e.g. speed, rhythm, variance) identified with
the new RMT system exhibited very high concurrent validity with the
gold-standard measurements (97.3\% of RMT measures were within +/-0.5Hz of the
Optotrak measures), and outperformed DLC and other advanced computer vision
tools (around 88.2\% of DLC measures were within +/-0.5Hz of the Optotrak
measures). RMT also accurately tracked a range of other rapid human movements
such as foot tapping, head turning and sit-to -stand movements.
  Conclusion: With the ubiquity of video technology in smart devices, the RMT
method holds potential to transform access and accuracy of human movement
assessment.
