Yazar "Eliiyi, Ugur" seçeneğine göre listele

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    Detection of Freezing of Gait Episodes in Patients with Parkinson's Disease using Electroencephalography and Motion Sensors: A Protocol and its Feasibility Results
    (Wolters Kluwer Medknow Publications, 2022) Eliiyi, Ugur; Kahraman, Turhan; Genc, Arzu; Keskinoglu, Pembe; Ozkurt, Ahmet; Donmez, BerrilColakoglu
    Objective: Freezing of gait (FOG) is an important concern for both patients with Parkinson's disease (pwPD) and physicians. In this study, we aimed to introduce a study protocol and our initial data. The data were subsequently used in machine learning models to detect FOG episodes using brain activity signals and motion data in the laboratory setting using complex FOG-evoking activities in a sample of pwPD with and without FOG compared with age-matched healthy controls. Subjects and Methods: An experimental task to evoke a FOG episode was designed. This experimental task was tested on two pwPD with FOG in on and off periods and one healthy control. Brain activity signals and motion data were collected simultaneously using electroencephalography (EEG) and inertial measurement units (IMUs). Results: The whole procedure took about 2 h, during which around 30 min were spent on walking tasks, involving 35 complete tours in the designed 8-m hallway by pwPD. Both EEG and IMUs sensor data could be collected, accompanied by FOG episode data marked by the neurologist. The video recordings of the patient's walking tasks were checked and reanalyzed by the neurologist sometime after the data experiment for marking the beginnings and ends of the observed FOG episodes more precisely. In the end, 24 stops were marked as FOG, which corresponded to 11% of the sensor data collected during the walking tasks. Conclusion: The designed FOG-evoking task protocol could be performed without any adverse effects, and it created enough FOG episodes for analysis. EEG and motion sensor data could be successfully collected without any significant artifacts.
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    A DYNAMIC PACKING APPROACH FOR INTERNET-OF-THINGS AND LOGISTICS APPLICATIONS
    (Inst Applied Mathematics, 2023) Eliiyi, Ugur; Nasibov, Efendi
    A novel dynamic rectangular packing model for a resource allocation problem, which has many applications in Logistics Internet-of-Things (L-IoT) is considered in this study. We use an optimization approach to deal with an IoT-based problem, whose objective is to max-imize the profit obtained from packing the data demand over a sequence of time frames, while satisfying several quality of service constraints. We propose a nonlinear integer programming model for the problem that optimizes demand partitioning and rectangle packing simultaneously, for the first time in literature. By introducing effective upper and lower bounds for this practi-cally important problem, a computational experimentation is designed for assessing the model and bound performances. An extensive discussion and recommendations for policy-making are included based on the computational results.