Oluwaleke Umar Yusuf
About
I am a PhD Research Fellow at NTNU's Department of Engineering Cybernetics, working under Professors Adil Rasheed and Frank Lindseth as part of the PERSEUS Doctoral Program in collaboration with Mobilitetslab Stor-Trondheim (MoST).
My research centres around using Big Data and AI to create Digital Twins of urban mobility infrastructures, with the goal of addressing challenges associated with achieving carbon-neutral mobility. Additionally, my research involves exploring cutting-edge technologies—such as Computer Vision and Generative AI/ML— and how they can contribute to the development of Future Mobility technologies.
I worked on “Skeleton-Based Hand Gesture Recognition (HGR) using Data-Level Fusion” for my MSc research thesis at the American University in Cairo, Egypt. The research involved developing a skeleton-based HGR framework that transformed the HGR task into an image classification task by encoding spatiotemporal gesture information into RGB images via data-level fusion. The framework was evaluated on benchmark datasets and demonstrated competitive performance with the SOTA.
I also produced several publications based on my MSc thesis and previous RA technical reports, including a IGI book chapter on the evolution of Blockchain Technology and its enabling technologies, another IGI book chapter exploring several temporal information condensation methods for HGR, and a IEEE ICMA journal paper on a lightweight real-time application for dynamic HGR.
Research
Publications
Evolution of Blockchain Technology: Principles, Research Trends & Challenges, Applications, and Future Directions
Transforming Hand Gesture Recognition into Image Classification Using Data Level Fusion: Methods, Framework & Results
Development of a Lightweight Real-Time Application for Dynamic Hand Gesture Recognition
As the author of several publications, I have explored diverse topics related to my MSc thesis and RA technical reports. One of the book chapters provides a comprehensive overview of Blockchain Technology, covering its principles, research trends, challenges, applications, and future directions. Another book chapter describes a skeleton-based HGR framework that leverages data-level fusion to transform the HGR task into an image classification task. This framework encodes spatiotemporal information from dynamic gestures into static representational images and was evaluated on several benchmark datasets. Additionally, I developed a lightweight real-time application for dynamic HGR that uses the skeleton-based framework and data-level fusion to achieve high classification accuracy with minimal CPU and RAM requirements. The paper was accepted for publication in the 2023 IEEE International Conference on Mechatronics and Automation.
2024
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Yusuf, Oluwaleke Umar;
Rasheed, Adil;
Lindseth, Frank.
(2024)
Unveiling Urban Mobility Patterns: A Data-Driven Analysis of Public Transit.
arXiv.org
Academic article
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Yusuf, Oluwaleke Umar;
Rasheed, Adil;
Lindseth, Frank.
(2024)
Exploring Urban Mobility Trends using Cellular Network Data.
arXiv.org
Academic article
Journal publications
-
Yusuf, Oluwaleke Umar;
Rasheed, Adil;
Lindseth, Frank.
(2024)
Unveiling Urban Mobility Patterns: A Data-Driven Analysis of Public Transit.
arXiv.org
Academic article
-
Yusuf, Oluwaleke Umar;
Rasheed, Adil;
Lindseth, Frank.
(2024)
Exploring Urban Mobility Trends using Cellular Network Data.
arXiv.org
Academic article