Mr. Zeev Kalyuzhner Profile

Zeev Kalyuzhner

at Bar-Ilan Univ

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Publications (2)

SPIE Journal Paper | 29 March 2024 Open Access

Remote and low-cost intraocular pressure monitoring by deep learning of speckle patterns

Zeev Kalyuzhner, Sergey Agdarov, Yevgeny Beiderman, Aviya Bennet, Yafim Beiderman, Zeev Zalevsky

JBO, Vol. 29, Issue 03, 037003, (March 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.3.037003

KEYWORDS: Mercury, Eye, Data modeling, Speckle pattern, Glaucoma, Education and training, Eye models, Deep learning, Sclera, Video

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Proceedings Article | 4 March 2022 Presentation

Wearable vs. contact-free photonic bio-sensors and their application to SARS-CoV-2 symptoms detection

Zeev Kalyuzhner, Yafim Beiderman, Sergey Agdarov, Zeev Zalevsky

Proceedings Volume PC11979, PC1197904 (2022) https://doi.org/10.1117/12.2614647

KEYWORDS: Sensors, Biosensors, Biomedical optics, Artificial intelligence, Evolutionary algorithms, Speckle pattern, Sclera, Oxygen, Optical sensing, Lung

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In this presentation we will present two types of sensors for detecting SARS-CoV-2 symptoms. The first part of the presentation will address a contact-free sensor while its operation principle involves illuminating the inspected subject with a laser beam and analyzing with artificial intelligence (AI) based algorithms, the temporal-spatial changes occurring in the back scattered secondary 2D speckle patterns captured through properly defocused optics. The sensing is performed from a distance of several meters away and is applied to different regions of the subject’s body. We demonstrate measurements performed from the chest and then we extract various cardio-pulmonary bio-sign (several simultaneously) including the sounds of subject’s heart and lungs (like a remote stethoscope). We also perform measurements from the sclera and search for anomalies in the random eye movements. From those anomalies we estimate amount of saturated oxygen in the blood stream. All of the above-mentioned bio-parameters could be useful for remote early detection of SARS-CoV-2 symptoms. The AI algorithms are applied not only to extract the various bio-signs but also to perform the bio-medical diagnosis. In the second part of the presentation, we will present fiber based sensor that is incorporated into textile and clothing and make them a smart-clothing capable via a non-tight contact way to perform sensing of various vital bio-signs (several simultaneously). The bio-parameters to be sensed are related to cardio-pulmonary activity as well as blood-pressure and thus could be associated with early detection of SARS-CoV-2 symptoms. The fiber sensor is based on enhanced multi-mode fiber while at its output an artificial intelligence (AI) based algorithm analyses the temporal-spatial characterizations of the generated dynamic 2D speckle patterns. The fiber sensors are positioned in several locations in the clothing and can perform the bio-measurement from different organs of the wearer and thus allow a comparative measurement which could assist in obtaining more agnostic and more reliable bio-sensing. The AI algorithms are applied not only to extract the various bio-signs but also to perform the bio-medical diagnosis.

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