Development of a markerless optical motion capture system for daily use of training in swimming

Ferryanto, F.; Nakashima, Motomu

doi:10.1007/s12283-016-0218-6

Cited by 11 publications

(10 citation statements)

References 13 publications

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“…Regarding the used data for the development and testing of the systems, on the one hand, several papers such as [ 12 , 13 , 14 , 15 , 17 , 18 , 20 , 37 , 38 , 42 ], developed their own datasets using manual annotations, MoCap systems, or other ground truth generation methods, but did not make them publicly available. Other papers created and published their dataset to contribute to the research community, such as [ 22 , 30 ].…”

Section: Discussionmentioning

confidence: 99%

“… RGB images (+ output of HPE using a method from other paper) Output: 3D position of the CoM Own not public data using 5 GoPro cameras (used to reconstruct the 3D position, no pose data is stored) and a force plate , being this last one element the one that gives the position of the CoM to compare with the result of the system. Development of a markerless optical motion capture system for daily use of training in swimming [ 38 ] Estimation of the pose and rotation and velocity of joints of swimmers, and fluid force simulation. RGB images Output: 2D Own not public data recorded using a single static camera underwater recording the swimmer performing butterfly stroke.…”

Section: Table A1mentioning

confidence: 99%

“…Development of a markerless optical motion capture system for daily use of training in swimming [38] Estimation of the pose and rotation and velocity of joints of swimmers, and fluid force simulation.…”

Section: Appendix a Datamentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

Badiola-Bengoa

Zorrilla

2021

Sensors

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Human Pose Estimation (HPE) has received considerable attention during the past years, improving its performance thanks to the use of Deep Learning, and introducing new interesting uses, such as its application in Sport and Physical Exercise (SPE). The aim of this systematic review is to analyze the literature related to the application of HPE in SPE, the available data, methods, performance, opportunities, and challenges. One reviewer applied different inclusion and exclusion criteria, as well as quality metrics, to perform the paper filtering through the paper databases. The Association for Computing Machinery Digital Library, Web of Science, and dblp included more than 500 related papers after the initial filtering, finally resulting in 20. In addition, research was carried out regarding the publicly available data related to this topic. It can be concluded that even if related public data can be found, much more data is needed to be able to obtain good performance in different contexts. In relation with the methods of the authors, the use of general purpose systems as base, such as Openpose, combined with other methods and adaptations to the specific use case can be found. Finally, the limitations, opportunities, and challenges are presented.

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Section: Discussionmentioning

confidence: 99%

Section: Table A1mentioning

confidence: 99%

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A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

Badiola-Bengoa

Zorrilla

2021

Sensors

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“…One of the proposed methods for dealing with those problems is introducing a markerless optical motion capture system. The markerless optical motion capture system has recently been developed to analyze human gait [14] and swimming motion [15,16].…”

Section: Introductionmentioning

confidence: 99%

Development of a Markerless Optical Motion Capture System by an Action Sports Camera for Running Motion

Ferryanto

Mahyuddin²,

Nakashima³

2022

AEJ

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A marker-based optical motion capture system is often used to obtain the kinematics parameters of a running analysis. However, the attached marker could affect the participant's movement, and the system is costly because of the exclusive cameras. Due to its drawbacks, the present research aimed to develop an affordable markerless optical motion capture system for running motion. The proposed system used an action sports camera to acquire the running images of the participant. The images were segmented to get the silhouette of the participant. Then, a human body model was generated to provide a priori information to track participants' segment position. The subsequent procedure was image registration to estimate the pose of the participant's silhouette. The transformation parameters were estimated by particle swarm optimization. The optimization output in the form of the rotation angle of the body segment was then employed to identify right or left lower limbs. To validate the results of the optimization, a manual matching was conducted to obtain the actual rotation angle for all body segments. The correlation coefficient between the rotation angle from image registration and the actual rotation angle was then evaluated. It was found that the lowest correlation coefficient was 0.977 for the left foot. It implies that the accuracy of the developed system in the present work is acceptable. Furthermore, the results of the kinematics analysis have good agreement with the literature. Therefore, the developed system, not only yields acceptable running parameters, but also affordable since it uses an action sports camera and easy to use.

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“…There are many experimental methods using human as a subject to learn the mechanics in human swimming. For example, equipping a pressure sensor glove to the swimmer to measure the propulsive forces [1], putting a spring wire on a swimmer to measure the drag [2], or using motion capture system to obtain and analyze the swimming motion [3,4]. These methods require motion consistency and precision by the subject to have a better understanding of the mechanics.…”

Section: Introductionmentioning

confidence: 99%

Realization and Swimming Performance of Backstroke by the Swimming Humanoid Robot

Razi

Nakashima

2019

J. Abmech

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The objective of this study was to realize the backstroke with the swimming humanoid robot. By realizing the backstroke, the backstroke performance of the robot was assessed regarding the speed and the swimming behavior. The motion for the robot was generated based on the motion of an actual swimmer. However, since the robot did not have sufficient degreesof-freedom to realize the motion, the actual motion was modified. The motion was investigated by simulation and later by the experiment. In the experiment, the robot successfully realized the backstroke at the stroke cycle 2.3 s with a swimming speed of 0.28 m/s.

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Development of a markerless optical motion capture system for daily use of training in swimming

Cited by 11 publications

References 13 publications

A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

Development of a Markerless Optical Motion Capture System by an Action Sports Camera for Running Motion

Realization and Swimming Performance of Backstroke by the Swimming Humanoid Robot

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