Author, Institution: Roseline Oluwaseun Ogundokun, Kaunas University of Technology
Science area, field of science: Technological Sciences, Informatics Engineering, T007
Research supervisor: Prof. Dr. Rytis Maskeliūnas (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007)
Dissertation Defence Board of Informatics Engineering Science Field:
Prof. Dr. Rimantas Butleris (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007) – chairperson
Prof. Dr. Hab. Romualdas Baušys (Vilnius Gediminas Technical University, Technological Sciences, Informatics Engineering, T007)
Prof. Dr. Varadraj Gurupur (University of Central Florida, United States, Natural Sciences, Informatics, N009)
Prof. Dr. Arnas Kačeniauskas (Vilnius Gediminas Technical University, Technological Sciences, Informatics Engineering, T007)
Prof. Dr. Filippo Sanfilippo (University of Agder, Norway, Natural Sciences, Informatics, N 009)
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: R. O. Ogundokun el. dissertation.pdf
© R. O. Ogundokun, 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 doctoral dissertation examines effective approaches for detecting and analysing human posture from occluded and limited video data, with particular attention to challenging real-world conditions such as occlusion, low image resolution, and constrained visibility, which commonly affect video-based monitoring environments. These challenges frequently arise in healthcare monitoring and intelligent digital systems and often reduce the reliability of conventional posture recognition methods. To address these issues, the dissertation developed optimized deep learning models based on hyperparameter-tuned transfer learning, data augmentation, and lightweight convolutional neural network architectures. Hybrid models that combine deep learning feature extraction with machine learning classifiers are also introduced to improve accuracy, generalization and computational efficiency. The proposed models are extensively evaluated on benchmark and real-world datasets, consistently demonstrating improved detection performance compared to existing conventional deep learning models. The results demonstrated that the proposed models achieved higher accuracy, robustness, and efficiency while remaining suitable for deployment in resource-constrained healthcare and smart monitoring systems. The dissertation contributes novel methodological insights and practical solutions for posture detection in digital health application