Three-Dimensional Motion Capture Data of a Movement Screen from 183 Athletes
Abstract Movement screens are widely used to identify aberrant movement patterns in hopes of decreasing risk of injury, identifying talent, and/or improving performance. Motion capture data can provide quantitative, objective feedback regarding movement patterns. The dataset contains three-dimension...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-04-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02082-6 |
| _version_ | 1827956967325302784 |
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| author | Xiong Zhao Gwyneth Ross Brittany Dowling Ryan B. Graham |
| author_facet | Xiong Zhao Gwyneth Ross Brittany Dowling Ryan B. Graham |
| author_sort | Xiong Zhao |
| collection | DOAJ |
| description | Abstract Movement screens are widely used to identify aberrant movement patterns in hopes of decreasing risk of injury, identifying talent, and/or improving performance. Motion capture data can provide quantitative, objective feedback regarding movement patterns. The dataset contains three-dimensional (3D) motion capture data of 183 athletes performing mobility tests (ankle, back bend, crossover adduction, crossover rotation, elbows, head, hip turn, scorpion, shoulder abduction, shoulder azimuth, shoulder rotation, side bends, side lunges and trunk rotation) and stability tests (drop jump, hop down, L-cut, lunge, rotary stability, step down and T-balance) bilaterally (where applicable), the athletes’ injury history, and demographics. All data were collected at 120 Hz or 480 Hz using an 8-camera Raptor-E motion capture system with 45 passive reflective markers. A total of 5,493 trials were pre-processed and included in .c3d and .mat formats. This dataset will enable researchers and end users to explore movement patterns of athletes of varying demographics from different sports and competition levels; develop objective movement assessment tools; and gain new insights into the relationships between movement patterns and injury. |
| first_indexed | 2024-04-09T15:12:46Z |
| format | Article |
| id | doaj.art-56a0c85921e84e94ab9e7a8e6045fc5e |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-04-09T15:12:46Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-56a0c85921e84e94ab9e7a8e6045fc5e2023-04-30T11:07:03ZengNature PortfolioScientific Data2052-44632023-04-0110111110.1038/s41597-023-02082-6Three-Dimensional Motion Capture Data of a Movement Screen from 183 AthletesXiong Zhao0Gwyneth Ross1Brittany Dowling2Ryan B. Graham3School of Human Kinetics, Faculty of Health Sciences, University of OttawaSchool of Human Kinetics, Faculty of Health Sciences, University of OttawaMotus Global, LLC., Rockville CentreSchool of Human Kinetics, Faculty of Health Sciences, University of OttawaAbstract Movement screens are widely used to identify aberrant movement patterns in hopes of decreasing risk of injury, identifying talent, and/or improving performance. Motion capture data can provide quantitative, objective feedback regarding movement patterns. The dataset contains three-dimensional (3D) motion capture data of 183 athletes performing mobility tests (ankle, back bend, crossover adduction, crossover rotation, elbows, head, hip turn, scorpion, shoulder abduction, shoulder azimuth, shoulder rotation, side bends, side lunges and trunk rotation) and stability tests (drop jump, hop down, L-cut, lunge, rotary stability, step down and T-balance) bilaterally (where applicable), the athletes’ injury history, and demographics. All data were collected at 120 Hz or 480 Hz using an 8-camera Raptor-E motion capture system with 45 passive reflective markers. A total of 5,493 trials were pre-processed and included in .c3d and .mat formats. This dataset will enable researchers and end users to explore movement patterns of athletes of varying demographics from different sports and competition levels; develop objective movement assessment tools; and gain new insights into the relationships between movement patterns and injury.https://doi.org/10.1038/s41597-023-02082-6 |
| spellingShingle | Xiong Zhao Gwyneth Ross Brittany Dowling Ryan B. Graham Three-Dimensional Motion Capture Data of a Movement Screen from 183 Athletes Scientific Data |
| title | Three-Dimensional Motion Capture Data of a Movement Screen from 183 Athletes |
| title_full | Three-Dimensional Motion Capture Data of a Movement Screen from 183 Athletes |
| title_fullStr | Three-Dimensional Motion Capture Data of a Movement Screen from 183 Athletes |
| title_full_unstemmed | Three-Dimensional Motion Capture Data of a Movement Screen from 183 Athletes |
| title_short | Three-Dimensional Motion Capture Data of a Movement Screen from 183 Athletes |
| title_sort | three dimensional motion capture data of a movement screen from 183 athletes |
| url | https://doi.org/10.1038/s41597-023-02082-6 |
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