2018/09/10 by Silvia L. Pintea, Jian Zheng, Pintea, Silvia L. +11
Computer Science · Medicine · Neuroscience · #Computer Vision and Pattern Recognition (cs.CV) #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Human Pose and Action Recognition #Neurological disorders and treatments #cs.CV
paper · pdf · doi:10.48550/arxiv.1809.03218
Best paper at ECCV-2018 Workshop on Observing and Understanding Hands in Action
arxiv created 2018/09/10 · openalex publication_date 2018/09/10 · arxiv updated 2018/09/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.