Mr. Deep Pal Profile

Deep Pal

Research Scholar at IIT (ISM), Dhanbad

SPIE Involvement:

Author

Area of Expertise:

Optical Fiber Sensors , Machine Learning , Biosensors , Optoelectronic , Sensors , Structural Health Monitoring

Deep Pal specializes in Fiber Optic Sensing and is currently pursuing his Ph.D. in Optics at the Indian Institute of Technology (ISM) Dhanbad. His research interests include Optical Fiber Sensors, Fiber Bragg Gratings, Surface Plasmon Resonance, Integrated Photonics, Nonlinear Optics, Sensors, Distributed Optical Sensing, Structural Health Monitoring, Biosensors, Optoelectronics, and Artificial Intelligence (AI). He is a member of IEEE, SPIE, and OSA and is a fast learner with a passion for exploring new areas of research. Additionally, Deep Pal has programming expertise in Python and Matlab, with a focus on Machine Learning (ML) and AI. He was also awarded a scholarship in Israel under the Ph.D. sandwich program for one year, during which he gained valuable experience at Bar-Ilan University.

SPIE Journal Paper | 1 August 2023 Open Access

Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses

Deep Pal, Amitesh Kumar, Nave Avraham, Yoram Eisenbach, Yevgeny Beiderman, Sergey Agdarov, Yafim Beiderman, Zeev Zalevsky

JBO, Vol. 28, Issue 08, 087001, (August 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.8.087001

KEYWORDS: Glucose, Blood, Magnetism, Speckle pattern, Data modeling, Education and training, Skin, Speckle, Plasma, Reflection

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SignificanceDiabetes is a prevalent disease worldwide that can cause severe health problems. Accurate blood glucose detection is crucial for diabetes management, and noninvasive methods can be more convenient and less painful than traditional finger-prick methods.AimWe aim to report a noncontact speckle-based blood glucose measurement system that utilizes artificial intelligence (AI) data processing to improve glucose detection accuracy. The study also explores the influence of an alternating current (AC) induced magnetic field on the sensitivity and selectivity of blood glucose detection.ApproachThe proposed blood glucose sensor consists of a digital camera, an AC-generated magnetic field source, a laser illuminating the subject’s finger, and a computer. A magnetic field is applied to the finger, and a camera records the speckle patterns generated by the laser light reflected from the finger. The acquired video data are preprocessed for machine learning (ML) and deep neural networks (DNNs) to classify blood plasma glucose levels. The standard finger-prick method is used as a reference for blood glucose level classification.ResultsThe study found that the noncontact speckle-based blood glucose measurement system with AI data processing allows for the detection of blood plasma glucose levels with high accuracy. The ML approach gives better results than the tested DNNs as the proposed data preprocessing is highly selective and efficient.ConclusionsThe proposed noncontact blood glucose sensing mechanism utilizing AI data processing and a magnetic field can potentially improve glucose detection accuracy, making it more convenient and less painful for patients. The system also allows for inexpensive blood glucose sensing mechanisms and fast blood glucose screening. The results suggest that noninvasive methods can improve blood glucose detection accuracy, which can have significant implications for diabetes management. Investigations involving representative sampling data, including subjects of different ages, gender, race, and health status, could allow for further improvement.

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SPIE Journal Paper | 5 September 2022 Open Access

Non-invasive blood glucose sensing by machine learning of optic fiber-based speckle pattern variation

Deep Pal, Sergey Agadarov, Yevgeny Beiderman, Yafim Beiderman, Amitesh Kumar, Zeev Zalevsky

JBO, Vol. 27, Issue 09, 097001, (September 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.9.097001

KEYWORDS: Glucose, Blood, Magnetism, Speckle pattern, Machine learning, Speckle, Multimode fibers, Sensors, Skin, Education and training

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