Mean and standard deviation of predicted joint depth error for the second data set.

<p>Mean and standard deviation of predicted joint depth error for the second data set.</p>

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Bibliographic Details
Main Author:	Vytautas Abromavičius (22008573) (author)
Other Authors:	Ervinas Gisleris (22008576) (author), Kristina Daunoravičienė (18251583) (author), Jurgita Žižienė (22008579) (author), Artūras Serackis (20783472) (author), Rytis Maskeliūnas (10172664) (author)
Published:	2025
Subjects:	Biotechnology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified yet challenges remain wrist joint (< upper limb activities refine depth coordinates reducing error margins proposed system combines missing joint positions kalman filtering framework human pose estimation facilitating telerehabilitation applications experimental results demonstrate enabling objective assessment depth estimation errors deep learning approaches enhance temporal consistency scenarios involving occlusions remote patient monitoring improving tracking precision div >< p single camera setups rehabilitation monitoring patient progress integrates temporal camera systems whereas marker traditional marker time interpolation study provides study proposes spatial synchronization scalable solution rehabilitation exercises recent studies p </ occlusion conditions linear regression home rehabilitation frontal perspectives allowing real
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