TY - GEN
T1 - Hand-tremor frequency estimation in videos
AU - Pintea, Silvia L.
AU - Zheng, Jian
AU - Li, Xilin
AU - Bank, Paulina J.M.
AU - van Hilten, Jacobus J.
AU - van Gemert, Jan C.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Eulerian hand tremors
KW - Human tremor dataset
KW - Phase-based tremor frequency detection
KW - Video hand-tremor analysis
UR - http://www.scopus.com/inward/record.url?scp=85061758417&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-11024-6_14
DO - 10.1007/978-3-030-11024-6_14
M3 - Conference contribution
AN - SCOPUS:85061758417
SN - 978-303011023-9
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 213
EP - 228
BT - Computer Vision
A2 - Leal-Taixé, Laura
A2 - Roth, Stefan
PB - Springer Science+Business Media
CY - Cham
T2 - 15th European Conference on Computer Vision, ECCV 2018
Y2 - 8 September 2018 through 14 September 2018
ER -
