2026-09-20T10:00:31Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1232772021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131024
Anette Hosoi.
Muñiz, Pablo E.(Muñiz Aponte)
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2019-12-13T19:02:08Z
2019-12-13T19:02:08Z
2019
2019
https://hdl.handle.net/1721.1/123277
1130230981
Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (page 44).
Pose estimation, a computer vision technique, can be used to develop a quantitative feedback training tool for long jumping. Key performance indicators (KPIs) such as launch velocity would allow a long jumping athlete to optimize their technique while training. However, these KPIs need a prior knowledge of when the athlete jumped, referred to as the launch frame in the context of videos and computer vision. Thus, an algorithm for estimating the launch frame was made using the OpenPose Demo and Matlab. The algorithm estimates the launch frame to within 0.8±0.91 frames. Implementing the algorithm into a training tool would give an athlete real-time, quantitative feedback from a video. This process of developing an algorithm to flag an event can be used in other sports as well, especially with the rise of KPIs in the sports industry (e.g. launch angle and velocity in baseball).
by Pablo E. Muniz.
S.B.
S.B. Massachusetts Institute of Technology, Department of Mechanical Engineering
44 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
Detection of launch frame in long jump videos using computer vision and discreet computation
Thesis
application/pdf
2019-12-13T19:02:07Z
Publication
Bachelor
MechE
unknown
unknown
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<Abstract>Pose estimation, a computer vision technique, can be used to develop a quantitative feedback training tool for long jumping. Key performance indicators (KPIs) such as launch velocity would allow a long jumping athlete to optimize their technique while training. However, these KPIs need a prior knowledge of when the athlete jumped, referred to as the launch frame in the context of videos and computer vision. Thus, an algorithm for estimating the launch frame was made using the OpenPose Demo and Matlab. The algorithm estimates the launch frame to within 0.8±0.91 frames. Implementing the algorithm into a training tool would give an athlete real-time, quantitative feedback from a video. This process of developing an algorithm to flag an event can be used in other sports as well, especially with the rise of KPIs in the sports industry (e.g. launch angle and velocity in baseball).</Abstract>
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