Národní úložiště šedé literatury Nalezeno 2 záznamů.  Hledání trvalo 0.00 vteřin. 
App Supporting Strength Sports Training
Klem, Richard ; Tesařová, Alena (oponent) ; Herout, Adam (vedoucí práce)
This thesis presents a novel approach for strength sports performance analysis using a human pose estimation machine learning model. The implemented solution employs the RTMPose model to estimate keypoints, then derive the barbell position from the wrist coordinates, and compute performance metrics without requiring specific camera angles or visible weight plates. The proposed method enhances traditional resistance training by providing feedback and performance metrics such as mean velocity. The solution was proved effective in both gym and home environments, even without barbells. Extensive experiments demonstrate the robustness and wide usability of the solution. Comparison with the professional system Qualisys confirms the validity of the application results.
Interaktivní nástroj pro bike fitting využívající počítačové vidění
Kocman, Matej ; Hradiš, Michal (oponent) ; Beran, Vítězslav (vedoucí práce)
Bike fitting is the adjustment of a cyclist’s pose on the bike often with the help of video analysis with the aim of increasing comfort, preventing injury and performance optimisation. The goal of this thesis was to create a prototype application for bike fitting, which would follow up on existing applications of its kind and would offer a working solution for chosen problems. The application offers functionality for increased keypoint detection accuracy, lowering the number of position iterations needed for optimal bike setup and optimal pose setup for a bike with limited setup options. The application was tested on users and contributed towards reaching an ideal position after a few iterations already. The application is a useful tool for bike fitting at home and the thesis propositions ways of improving it further.

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