  We focus on the problem of estimating human hand-tremor frequency from input
RGB video data. Estimating tremors from video is important for non-invasive
monitoring, analyzing and diagnosing patients suffering from motor-disorders
such as Parkinson's disease. We consider two approaches for hand-tremor
frequency estimation: (a) a Lagrangian approach where we detect the hand at
every frame in the video, and estimate the tremor frequency along the
trajectory; and (b) an Eulerian approach where we first localize the hand, we
subsequently remove the large motion along the movement trajectory of the hand,
and we use the video information over time encoded as intensity values or phase
information to estimate the tremor frequency.
  We estimate hand tremors on a new human tremor dataset, TIM-Tremor,
containing static tasks as well as a multitude of more dynamic tasks, involving
larger motion of the hands. The dataset has 55 tremor patient recordings
together with: associated ground truth accelerometer data from the most
affected hand, RGB video data, and aligned depth data.
