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Hand-tremor frequency estimation in videos. / Pintea, Silvia L.; Zheng, Jian; Li, Xilin; Bank, Paulina J.M.; van Hilten, Jacobus J.; van Gemert, Jan C.

Computer Vision – ECCV 2018 Workshops, Proceedings. ed. / Laura Leal-Taixé; Stefan Roth. Vol. 11134 Springer Verlag, 2019. p. 213-228 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11134 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Pintea, SL, Zheng, J, Li, X, Bank, PJM, van Hilten, JJ & van Gemert, JC 2019, Hand-tremor frequency estimation in videos. in L Leal-Taixé & S Roth (eds), Computer Vision – ECCV 2018 Workshops, Proceedings. vol. 11134, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11134 LNCS, Springer Verlag, pp. 213-228, 15th European Conference on Computer Vision, ECCV 2018, Munich, Germany, 8/09/18. https://doi.org/10.1007/978-3-030-11024-6_14

APA

Pintea, S. L., Zheng, J., Li, X., Bank, P. J. M., van Hilten, J. J., & van Gemert, J. C. (2019). Hand-tremor frequency estimation in videos. In L. Leal-Taixé, & S. Roth (Eds.), Computer Vision – ECCV 2018 Workshops, Proceedings (Vol. 11134, pp. 213-228). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11134 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-11024-6_14

Vancouver

Pintea SL, Zheng J, Li X, Bank PJM, van Hilten JJ, van Gemert JC. Hand-tremor frequency estimation in videos. In Leal-Taixé L, Roth S, editors, Computer Vision – ECCV 2018 Workshops, Proceedings. Vol. 11134. Springer Verlag. 2019. p. 213-228. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-11024-6_14

Author

Pintea, Silvia L. ; Zheng, Jian ; Li, Xilin ; Bank, Paulina J.M. ; van Hilten, Jacobus J. ; van Gemert, Jan C. / Hand-tremor frequency estimation in videos. Computer Vision – ECCV 2018 Workshops, Proceedings. editor / Laura Leal-Taixé ; Stefan Roth. Vol. 11134 Springer Verlag, 2019. pp. 213-228 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{e790615d3a7f4f168864c811e1100eaf,
title = "Hand-tremor frequency estimation in videos",
abstract = "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.",
keywords = "Eulerian hand tremors, Human tremor dataset, Phase-based tremor frequency detection, Video hand-tremor analysis",
author = "Pintea, {Silvia L.} and Jian Zheng and Xilin Li and Bank, {Paulina J.M.} and {van Hilten}, {Jacobus J.} and {van Gemert}, {Jan C.}",
year = "2019",
doi = "10.1007/978-3-030-11024-6_14",
language = "English",
isbn = "978-303011023-9",
volume = "11134",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "213--228",
editor = "Laura Leal-Taix{\'e} and Stefan Roth",
booktitle = "Computer Vision – ECCV 2018 Workshops, Proceedings",

}

RIS

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

SN - 978-303011023-9

VL - 11134

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 – ECCV 2018 Workshops, Proceedings

A2 - Leal-Taixé, Laura

A2 - Roth, Stefan

PB - Springer Verlag

ER -

ID: 51682936