  Our hands reveal important information such as the pulsing of our veins which
help us determine the blood pressure, tremors indicative of motor control, or
neurodegenerative disorders such as Essential Tremor or Parkinson's disease.
The Computerized Classification of Micro-Motions in the hand using waveforms
from mobile phone videos is a novel method that uses Eulerian Video
Magnification, Skeletonization, Heatmapping, and the kNN machine learning model
to detect the micro-motions in the human hand, synthesize their waveforms, and
classify these. The pre-processing is achieved by using Eulerian Video
Magnification, Skeletonization, and Heat-mapping to magnify the micro-motions,
landmark essential features of the hand, and determine the extent of motion,
respectively. Following pre-processing, the visible motions are manually
labeled by appropriately grouping pixels to represent a particular label
correctly. These labeled motions of the pixels are converted into waveforms.
Finally, these waveforms are classified into four categories - hand or finger
movements, vein movement, background motion, and movement of the rest of the
body due to respiration using the kNN model. The final accuracy obtained was
around 92 percent.
