APPLICATION OF MACHINE LEARNING FOR MEASURING OF THE SUBJECT FITNESS
DOI:
https://doi.org/10.24867/30SA04VargaKeywords:
Machine learning, Computer vision, Computer graphicsAbstract
The aim of this thesis was to create a system capable of estimating the subject’s physical and cognitive fitness based on machine learning. It included analysis of motion detection systems and tools required for implementation. Using a single camera and Google’s MediaPipe, the system evaluates reaction time and jump height, optimized for ease-of-use and low resource requirements.
References
[1] V. Rago et al., Countermovement Jump Analysis Using Different Portable Devices, Sports, 2018
[2] T. Dingler et al., Building Cognition-Aware Systems, Proceedings of the ACM, 2017
[3] R. Josyula, A review on human pose estimation, arXiv:2110.06877, 2021
[4] W. Funk, Animac: Analog 3D Animation, 2010
[5] T. Baker, The History of Motion Capture Within The Entertainment Industry, Metropolia UAS, 2020
[6] https://lamho.wordpress.com/51-2/
[7] http://web.mit.edu/comm-forum/legacy/papers/furniss.html
[8] P.A. Nogueira, Motion Capture Fundamentals, Univ. of Porto, 2012
[9] M. Kok et al., An optimization-based approach to human body motion capture, IFAC, 2014
[10] Y. Chen et al., Monocular human pose estimation, CVIU, 2020
[11] C. Lugaresi et al., MediaPipe Framework, IEEE CVPR, 2019
[12] V. Bazarevsky et al., BlazePose: On-device Real-time Body Pose tracking, arXiv, 2020
