Author, Institution: Canbulut Cenker, Kaunas University of Technology
Science area, field of science: Technological Sciences, Informatics Engineering, T007
Research supervisor: Prof. Dr. Tomas Blažauskas (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007)
Dissertation Defence Board of Informatics Engineering Science Field:
Prof. Dr. Vidas Raudonis (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007) – chairperson
Prof. Dr. Rimantas Butleris (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007)
Prof. Dr. Ibrahim A. Hameed (Norwegian University of Science and Technology, Norway, Technological Sciences, Informatics Engineering, T007)
Prof. Dr. Arnas Kačeniauskas (Vilnius Gediminas Technical University, Technological Sciences, Informatics Engineering, T007)
Prof. Dr. Tomas Krilavičius (Vytautas Magnus University, Technological Sciences, Informatics Engineering, T007)
Dissertation defence meeting will be at Rectorate Hall of Kaunas University of Technology (K. Donelaičio 73–402, Kaunas)
The doctoral dissertation is available at the library of Kaunas University of Technology (Gedimino g. 50, Kaunas) and on the internet: C. Cenker el. dissertation.pdf
© C. Canbulut, 2026 “The text of the thesis may not be copied, distributed, published, made public, including by making it publicly available on computer networks (Internet), reproduced in any form or by any means, including, but not limited to, electronic, mechanical or other means. Pursuant to Article 25(1) of the Law on Copyright and Related Rights of the Republic of Lithuania, a person with a disability who has difficulties in reading a document of a thesis published on the Internet, and insofar as this is justified by a particular disability, shall request that the document be made available in an alternative form by e-mail to doktorantura@ktu.lt.”
Annotation: This dissertation investigates virtual reality (VR) control methods for peripheral device integration and human posture analysis using off-the-shelf VR controls. The research addresses challenges arising from the use of control inputs from devices not originally designed for VR, including data transmission latency, synchronisation inaccuracies, and visual stuttering, which affect the handling and interpretation of input data in VR systems. Data prediction methods such as interpolation, extrapolation, and filtering techniques are analysed and experimentally evaluated using an experimental virtual rowing system to assess their suitability for controlling motion in VR under controlled conditions. In addition, the dissertation examines control methods based on off-the-shelf VR tracking for human posture monitoring and movement analysis by presenting a processing workflow that includes positional data acquisition, transformation into joint-angle representations, and the application of machine learning methods for exercise detection and movement correctness classification. Random Forest and Convolutional Neural Network models are evaluated using eight predefined upper-body exercises, and the results provide an assessment of the applicability of VR-based control and data processing methods for posture evaluation in controlled experimental environments.