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Objective The coordination of human movement directly reflects function ofthe central nervous system. Small deficits in movement are often the first signof an underlying neurological problem. The objective of this research is todevelop a new end-to-end, deep learning-based system, Rapid-Motion-Track (RMT)that can track the fastest human movement accurately when webcams or laptopcameras are used.  Materials and Methods We applied RMT to finger tapping, a well-validated testof motor control that is one of the most challenging human motions to trackwith computer vision due to the small keypoints of digits and the highvelocities that are generated. We recorded 160 finger tapping assessmentssimultaneously with a standard 2D laptop camera (30 frames/sec) and ahigh-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 tappingfrequencies up to 8Hz to extract movement features.  Results The movement features (e.g. speed, rhythm, variance) identified withthe new RMT system exhibited very high concurrent validity with thegold-standard measurements (97.3\% of RMT measures were within +/-0.5Hz of theOptotrak measures), and outperformed DLC and other advanced computer visiontools (around 88.2\% of DLC measures were within +/-0.5Hz of the Optotrakmeasures). RMT also accurately tracked a range of other rapid human movementssuch as foot tapping, head turning and sit-to -stand movements.  Conclusion: With the ubiquity of video technology in smart devices, the RMTmethod holds potential to transform access and accuracy of human movementassessment.

Rapid-Motion-Track: Markerless Tracking of Fast Human Motion with Deeper Learning