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The research aim was to analyse the influence of velocity and size of markerson the accuracy of motion capture measurement utilising image processing withthe use of OpenCV. On the basis of the obtained results, the usefulness of theapplied measurement method in studying the kinematics of the human bodywhile driving operating a wheelchair was determined. This article presents thetest results for a low-budget motion capture measurement system for testingthe kinematics of the human body in a single plane. The tested measuringsystem includes a standard activity camera Xiaomi Yi4K, expanded polystyrenemarkers with printed ArUco codes, and original software for marker positiondetection developed by the author. The analysis of the measurement methodwith regard to its applicability in biomechanical studies has highlighted severalkey factors: the number of measuring points, measurement accuracy expressedas a relative error and the limit velocity at which the marker trajectory is correctlyrepresented. The article shows that the limit velocity of the marker is 2.2 m/sfor 50x50 mm markers and 1.4 m/s for 30x30 mm markers. The number ofmeasured points ranged from 233 to 2,457 depending on the marker velocity.The relative error did not exceed 5% for the marker velocities and thus provideda correct representation of its trajectory.

The effects of ArUco marker velocity and size on motion capture detection and accuracy in the context of human body kinematics analysis