Dataset Information

Three-dimensional motion capture data during repetitive overarm throwing practice.

ABSTRACT: Three-dimensional motion capture analysis is considered the gold standard for any movement research. Motion capture data were recorded for 7 healthy female participants with no prior throwing experience to investigate the learning process for overarm throwing during a selected period. Participants were monitored 3 times a week for 5 weeks. Each session consisted of 15 dominant and 15 nondominant hand side overarm throws. A total of 3,150 trials were recorded and preprocessed (labeling reflective markers) for further analysis. The presented dataset can provide valuable information about upper extremity kinematics of the learning process of overarm throwing without any kind of feedback. Furthermore, this dataset may be used for more advanced analysis techniques, which could lead to more insightful information.

SUBMITTER: Ozkaya G

PROVIDER: S-EPMC6278687 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Three-dimensional motion capture data during repetitive overarm throwing practice.

Ozkaya Gizem G Jung Hae Ryun HR Jeong In Sub IS Choi Min Ra MR Shin Min Young MY Lin Xue X Heo Woo Seong WS Kim Mi Sun MS Kim Eonho E Lee Ki-Kwang KK

Scientific data 20181204

Three-dimensional motion capture analysis is considered the gold standard for any movement research. Motion capture data were recorded for 7 healthy female participants with no prior throwing experience to investigate the learning process for overarm throwing during a selected period. Participants were monitored 3 times a week for 5 weeks. Each session consisted of 15 dominant and 15 nondominant hand side overarm throws. A total of 3,150 trials were recorded and preprocessed (labeling reflective ...[more]

PMID: 30512013

Similar Datasets

Project description:BackgroundThree-dimensional joint kinematics during canine locomotion are commonly measured using skin marker-based stereophotogrammetry technologies. However, marker-related errors caused by the displacement of the skin surface relative to the underlying bones (i.e., soft tissue artifacts, STA) may affect the accuracy of the measurements and obscure clinically relevant information. Few studies have assessed STA in canine limbs during kinematic analysis. The magnitudes and patterns of the STA and their influence on kinematic analysis remain unclear. Therefore, the current study aims to quantify the in vivo STA of skin markers on the canine thigh and crus during passive joint motion. The stifle joints of ten dogs were passively extended while the skin markers were measured using a motion capture system, and skeletal kinematics were determined using a CT-to-fluoroscopic image registration method.ResultsThe skin markers exhibited considerable STA relative to the underlying bones, with a peak amplitude of 27.4?mm for thigh markers and 28.7?mm for crus markers; however, the amplitudes and displacement directions at different attachment sites were inconsistent. The markers on the cranial thigh and lateral crus closer to the stifle joint had greater STA amplitudes in comparison to those of other markers. Most markers had STA with linear and quadratic patterns against the stifle flexion angles. These STA resulted in underestimated flexion angles but overestimated adduction and internal rotation when the stifle was flexed to greater than 90°.ConclusionsMarker displacements relative to the underlying bones were prominent in the cranial aspect of the thigh and the proximal-lateral aspect of the crus. The calculated stifle kinematic variables were also affected by the STA. These findings can provide a reference for marker selection in canine motion analysis for similar motion tasks and clarify the relationship between STA patterns and stifle kinematics; the results may therefore contribute to the development of STA models and compensation techniques for canine motion analysis.

Project description:Purpose:To describe an unsupervised three-dimensional cardiac motion estimation network (CarMEN) for deformable motion estimation from two-dimensional cine MR images. Materials and Methods:A function was implemented using CarMEN, a convolutional neural network that takes two three-dimensional input volumes and outputs a motion field. A smoothness constraint was imposed on the field by regularizing the Frobenius norm of its Jacobian matrix. CarMEN was trained and tested with data from 150 cardiac patients who underwent MRI examinations and was validated on synthetic (n = 100) and pediatric (n = 33) datasets. CarMEN was compared to five state-of-the-art nonrigid body registration methods by using several performance metrics, including Dice similarity coefficient (DSC) and end-point error. Results:On the synthetic dataset, CarMEN achieved a median DSC of 0.85, which was higher than all five methods (minimum-maximum median [or MMM], 0.67-0.84; P < .001), and a median end-point error of 1.7, which was lower than (MMM, 2.1-2.7; P < .001) or similar to (MMM, 1.6-1.7; P > .05) all other techniques. On the real datasets, CarMEN achieved a median DSC of 0.73 for Automated Cardiac Diagnosis Challenge data, which was higher than (MMM, 0.33; P < .0001) or similar to (MMM, 0.72-0.75; P > .05) all other methods, and a median DSC of 0.77 for pediatric data, which was higher than (MMM, 0.71-0.76; P < .0001) or similar to (MMM, 0.77-0.78; P > .05) all other methods. All P values were derived from pairwise testing. For all other metrics, CarMEN achieved better accuracy on all datasets than all other techniques except for one, which had the worst motion estimation accuracy. Conclusion:The proposed deep learning-based approach for three-dimensional cardiac motion estimation allowed the derivation of a motion model that balances motion characterization and image registration accuracy and achieved motion estimation accuracy comparable to or better than that of several state-of-the-art image registration algorithms.© RSNA, 2019Supplemental material is available for this article.

Dataset Information

Three-dimensional motion capture data during repetitive overarm throwing practice.

Publications

Three-dimensional motion capture data during repetitive overarm throwing practice.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure