%0 Journal Article 
%A Ruiz Zafra, Ángel
%A Pigueiras del Real, Janet
%A Heredia-Jimenez, Jose
%A Shah, Syed Taimoor Hussain
%A Shah, Syed Adil Hussain
%T PyBodyTrack: A python library for multi-algorithm motion quantification and tracking in videos
%D 2025
%@ 2352-7110
%U http://hdl.handle.net/10498/37866
%X Human movement analysis, driven by computer vision and pose tracking technologies, is gaining acceptance
in healthcare, rehabilitation, sports, and daily activity monitoring. While most approaches focus on qualitative
analysis (e.g., pattern recognition), objective motion quantification can provide valuable insights for diagnosis,
progress tracking, and performance assessment. This paper introduces PyBodyTrack, a Python library for motion
quantification using mathematical methods in real-time and pre-recorded videos. It simplifies video management
and integrates with position estimators like MediaPipe, YOLO, and OpenPose. PyBodyTrack enables seamless
motion quantification through standardized metrics, facilitating its integration into various applications.
%K Motion quantification
%K Pose tracking
%K Computer vision
%K Human movement analysis
%K Python library
%~ Universidad de Cádiz