Hand-tremor frequency estimation in videos
This work addresses non-invasive tremor monitoring for patients with motor disorders, but it appears incremental as it applies existing Lagrangian and Eulerian methods to a new dataset.
The paper tackled the problem of estimating hand-tremor frequency from RGB videos for non-invasive monitoring of motor disorders like Parkinson's disease, and it introduced a new dataset, TIM-Tremor, with 55 patient recordings and ground truth accelerometer data.
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.