The use of AI in Human Pose Estimation Applications in Kinesiology: Taxonomy of Algorithms, Models, and Evaluation Methods

• Published in: 2024 47th MIPRO ICT and Electronics Convention (MIPRO) pp. 1163 - 1166
• Main Authors: Katovic, D., Bronzin, T., Horvat, M., Prole, B., Stipic, A., Jelaca, N., Pavlovic, I., Pap, K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 20.05.2024
• Subjects: action recognition; computer vision; Games; motion estimation; motion tracking; multi-person pose estimation; neural networks; Pose estimation; Reviews; single person pose estimation; Symbiosis; Taxonomy; Tracking; Visualization
• ISSN: 2623-8764
• DOI: 10.1109/MIPRO60963.2024.10569385

Kinesiology and its related disciplines (kinanthropometry, biomechanics, kinesiological rehabilitation, sports games) symbiotically attract new computer technologies, implementing them in the form of effective tools for analysis, diagnosis, and assessment of the state of the subject or team.The use of neural networks and computer vision integrated into pose estimation technology can be used by kinesiology as an applicable integration of knowledge from the domain of movement analysis, analysis of sports games, rehabilitation, and education into an environment of computer-transformed visual information and patterns into a form suitable for further analytical processes.The paper represents a structured review of algorithms, models, and evaluation methods for recognizing and monitoring human movement, single or multi-person, in real-time using human pose estimation.