Title Enhanced human skeleton tracking for improved joint position and depth accuracy in rehabilitation exercises /
Authors Abromavičius, Vytautas ; Gisleris, Ervinas ; Daunoravičienė, Kristina ; Žižienė, Jurgita ; Serackis, Artūras ; Maskeliūnas, Rytis
DOI 10.3390/app15020906
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Is Part of Applied sciences.. Basel : MDPI. 2025, vol. 15, iss. 2, art. no. 906, p. 1-23.. ISSN 2076-3417
Keywords [eng] deep learning ; pose estimation ; rehabilitation ; video stream
Abstract [eng] The objective of this work is to develop a method for tracking human skeletal movements by integrating data from two synchronized video streams. To achieve this, two datasets were created, each consisting of four different rehabilitation exercise videos featuring various individuals in diverse environments and wearing different clothing. The prediction model is employed to create a dual-image stream system that enables the tracking of joint positions even when a joint is obscured in one of the streams. This system also mitigates depth coordinate errors by using data from both video streams. The final implementation successfully corrects the positions of the right elbow and wrist joints, though some depth error persists in the left hand. The results demonstrate that adding a second video camera, rotated 90° and aimed at the subject, can compensate for depth prediction inaccuracies, reducing errors by up to 0.4 m. By using a dual-camera setup and fusing the predicted human skeletal models, it is possible to construct a complete human model even when one camera does not capture all body parts and to refine depth coordinates through error correction using a linear regression model.
Published Basel : MDPI
Type Journal article
Language English
Publication date 2025
CC license CC license description