Markerless camera-based vertical jump height measurement using OpenPose

Webering, Fritz; Blume, Holger; Allaham, Issam

doi:10.1109/cvprw53098.2021.00428

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…Both MMC and OMC are measured using the vertical displacement of the toe. As shown in Table III, both MMC RM M and MMC P T M achieve results comparable with the work of [21], which was also evaluated using OMC equipment. It is worth noting that our PTM approach assumes a simpler setup without manual calibration.…”

Section: B Resultsmentioning

confidence: 55%

“…Both MMC and OMC are measured using the vertical displacement of the toe. As shown in Table III , both MMC \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$_{RMM}$\end{document} and MMC \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$_{PTM}$\end{document} achieve results comparable with the work of [21] , which was also evaluated using OMC equipment. It is worth noting that our PTM approach assumes a simpler setup without manual calibration.…”

Section: Resultsmentioning

confidence: 99%

“…For quantitative comparison, we use the intraclass correlation coefficient [19] (ICC) and Bland-Altman analysis [20] (BA). ICC and BA are often used for comparing new methods of measurements with a gold standard [9] , [14] , [21] .…”

Section: Discussionmentioning

confidence: 99%

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Quantifying Jump Height Using Markerless Motion Capture with a Single Smartphone

Aderinola

Younesian

Whelan

et al. 2023

IEEE Open J. Eng. Med. Biol.

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Goal: The countermovement jump (CMJ) is commonly used to measure lower-body explosive power. This study evaluates how accurately markerless motion capture (MMC) with a single smartphone can measure bilateral and unilateral CMJ jump height. Methods: First, three repetitions each of bilateral and unilateral CMJ were performed by sixteen healthy adults (mean age: 30.87 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\pm$\end{document} 7.24 years; mean BMI: 23.14 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\pm$\end{document} 2.55 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\text{kg/m}^{2}$\end{document} ) on force plates and simultaneously captured using optical motion capture (OMC) and one smartphone camera. Next, MMC was performed on the smartphone videos using OpenPose. Then, we evaluated MMC in quantifying jump height using the force plate and OMC as ground truths. Results: MMC quantifies jump heights with ICC between 0.84 and 0.99 without manual segmentation and camera calibration. Conclusions: Our results suggest that using a single smartphone for markerless motion capture is promising.

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Section: B Resultsmentioning

confidence: 55%

Section: Resultsmentioning

confidence: 99%

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Quantifying Jump Height Using Markerless Motion Capture with a Single Smartphone

Aderinola

Younesian

Whelan

et al. 2023

IEEE Open J. Eng. Med. Biol.

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“…Considering the broad emerging literature in markerless motion analysis, few studies have focused on balance related quantities, and, when doing so, the authors mainly consider the CoM and its derivatives [23][24][25][26][27][28]. To the best of our knowledge, no study has been conducted regarding balance related quantities using Theia3D.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of a markerless motion capture system for balance related quantities

Chaumeil

Lahkar

Dumas

et al. 2022

Preprint

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Background Balance studies usually focus on quantities describing the global body motion, such as the position of the whole-body centre of mass (CoM), its associated extrapolated centre of mass (XCoM) and the whole-body angular momentum (WBAM). Assessing such quantities using classical marker-based approach can be tedious and modify the participant's behaviour. The recent development of markerless motion capture methods could bypass the issues related to the use of markers. Research question Can we use markerless motion capture systems to study quantities that are relevant for balance studies? Methods Sixteen young healthy participants performed four different motor tasks: walking at self-selected speed, balance loss, walking on a narrow beam and countermovement jumps. Their movements were recorded simultaneously by marker-based and markerless motion capture systems. Videos were processed using a commercial markerless pose estimation software, Theia3D. The position of their CoM was computed, and the associated XCoM and WBAM were derived. Bland-Altman analysis was performed and root mean square error and coefficient of determination were computed to compare the results obtained with marker-based and markerless methods across all participants and tasks. Results Bias remained of the magnitude of a few mm for CoM and XCoM position, and RMSE of CoM and XCoM was around 1 cm. Confidence interval for CoM and XCoM was under 2 cm except for one task in one direction. RMSE of the WBAM was less than 8% of the total amplitude in any direction, and bias was less than 1%. Significance Results suggest that the markerless motion capture system can be used in balance studies as the measured errors are in the range of the differences found between different models or populations in the literature. Nevertheless, one should be careful when assessing dynamic movements such as jumping, as they displayed the biggest errors.

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Automatic Markerless Motion Detector Method against Traditional Digitisation for 3-Dimensional Movement Kinematic Analysis of Ball Kicking in Soccer Field Context

Vieira

Santiago

Pinto

et al. 2022

IJERPH

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Kicking is a fundamental skill in soccer that often contributes to match outcomes. Lower limb movement features (e.g., joint position and velocity) are determinants of kick performance. However, obtaining kicking kinematics under field conditions generally requires time-consuming manual tracking. The current study aimed to compare a contemporary markerless automatic motion estimation algorithm (OpenPose) with manual digitisation (DVIDEOW software) in obtaining on-field kicking kinematic parameters. An experimental dataset of under-17 players from all outfield positions was used. Kick attempts were performed in an official pitch against a goalkeeper. Four digital video cameras were used to record full-body motion during support and ball contact phases of each kick. Three-dimensional positions of hip, knee, ankle, toe and foot centre-of-mass (CMfoot) generally showed no significant differences when computed by automatic as compared to manual tracking (whole kicking movement cycle), while only z-coordinates of knee and calcaneus markers at specific points differed between methods. The resulting time-series matrices of positions (r2 = 0.94) and velocity signals (r2 = 0.68) were largely associated (all p < 0.01). The mean absolute error of OpenPose motion tracking was 3.49 cm for determining positions (ranging from 2.78 cm (CMfoot) to 4.13 cm (dominant hip)) and 1.29 m/s for calculating joint velocity (0.95 m/s (knee) to 1.50 m/s (non-dominant hip)) as compared to reference measures by manual digitisation. Angular range-of-motion showed significant correlations between methods for the ankle (r = 0.59, p < 0.01, large) and knee joint displacements (r = 0.84, p < 0.001, very large) but not in the hip (r = 0.04, p = 0.85, unclear). Markerless motion tracking (OpenPose) can help to successfully obtain some lower limb position, velocity, and joint angular outputs during kicks performed in a naturally occurring environment.

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Markerless camera-based vertical jump height measurement using OpenPose

Cited by 3 publications

References 19 publications

Quantifying Jump Height Using Markerless Motion Capture with a Single Smartphone

Quantifying Jump Height Using Markerless Motion Capture with a Single Smartphone

Accuracy of a markerless motion capture system for balance related quantities

Automatic Markerless Motion Detector Method against Traditional Digitisation for 3-Dimensional Movement Kinematic Analysis of Ball Kicking in Soccer Field Context

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