Researcher:
Hayırlıoğlu, Yusuf Ziya

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Master Student

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Yusuf Ziya

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Hayırlıoğlu

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Hayırlıoğlu, Yusuf Ziya

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2020 - 20243

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Researcher Of Publications: search.filters.isAuthorOfPublication.733e09ec-d7bf-4e6f-939d-9eaf430d448c

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    PhysioPatch: a multimodal and adaptable wearable patch for cardiovascular and cardiopulmonary assessment
    (IEEE-Inst Electrical Electronics Engineers Inc, 2024)  ; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Hayırlıoğlu, Yusuf Ziya; Gürsoy, Beren Semiz;  ; Graduate School of Sciences and Engineering; College of Engineering;  
    Remote monitoring systems offer significant advantages in assessing cardiovascular and cardiopulmonary health, facilitating early diagnosis and enabling personalized treatment plans. In this article, we present a novel wearable patch, PhysioPatch, which could facilitate comprehensive monitoring of cardiovascular and cardiopulmonary functions by simultaneously capturing various physiological signals, including electrocardiogram (ECG), seismocardiogram (SCG), photoplethysmogram (PPG), and body temperature. The design comprises a main body intended for placement on the mid-sternum and a detachable daughter body, enabling distal measurements to enhance comprehensive assessment. While the main body includes the sensors for measuring the body temperature, ECG, proximal PPG and SCG signals, and other electronics such as the microcontroller, the battery, the battery management system (BMS), the Bluetooth, and the microSD card;the daughter body houses the sensors for distal pulse vibration and PPG signal acquisition. Along with the system design, the algorithms to derive various hemodynamic parameters (heart rate (HR), HR variability (HRV), respiration rate, and oxygen saturation) are also presented. The system was validated with a human subject study including 20 participants, and the results have revealed that the PhysioPatch is capable of achieving high-quality signals, resulting in accurate derivation of hemodynamic parameters. Overall, such a system could potentially offer continuous health monitoring outside clinical settings, regardless of time and environmental stressors.
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    The effect of wearable technology on psychomotor agitation in patients with diagnostic patients with schizophrenia expansion and psychosis
    (Cambridge Univ Press, 2023) Aydin, P. Cetinay; Tokatlıoğlu, T. Şahin; Eser, E.; Zemen, N.; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Oflaz, Fahriye; Gürsoy, Beren Semiz; Hayırlıoğlu, Yusuf Ziya; School of Nursing; College of Engineering; Graduate School of Sciences and Engineering
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    Artificial intelligence-based identification of butter variations as a model study for detecting food adulteration
    (Elsevier Sci Ltd, 2020) Ergen, Onur; N/A; N/A; N/A; İymen, Gökçe; Tanrıver, Gizem; Hayırlıoğlu, Yusuf Ziya; Master Student; Master Student; Master Student; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; N/A; N/A; N/A
    The demand for high-quality food products is increasing globally at unprecedented rates in response to growing health concerns and consumer awareness about healthy food options. Yet, the tools for determining food quality remain restricted to well-equipped laboratories, not readily accessible to consumers. Unfortunately, the current inspection mechanisms are limited and cannot keep track of all the products continuously, which exposes weakness in the system towards adulteration, falsification, and mislabeling products. Consumers rely on manufacturer labeling alone, with no convenient and user-friendly tool to confirm quality, especially for organic products. The advancement of Artificial Intelligence (AI) provides an opportunity for these tools to be developed. In this study, we demonstrate that simple sound vibrations traversing the food products can be used in conjunction with deep learning models to verify high quality products with no additives, as well as organic food products. Our neural network models, namely Parallel CNN-RNN and CRNN, achieve high accuracy on the defined classification tasks. To our knowledge, this is the first report of an AI-based tool utilizing simple sound vibrations to identify adulteration in food products.