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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Towards non-invasive blood glucose measurement using machine learning: An all-purpose PPG system design
Shantanu Sen Gupta, Tae-Ho Kwon, Shifat Hossain, et al.
Biomedical Signal Processing and Control (2021) Vol. 68, pp. 102706-102706
Closed Access | Times Cited: 66

Showing 1-25 of 66 citing articles:

Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML
Mohammad Zeynali, Khalil Alipour, Bahram Tarvirdizadeh, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 2
Stress fusion evaluation modeling and verification based on non-invasive blood glucose biosensors for live fish waterless transportation
Yongjun Zhang, Xinqing Xiao, Huanhuan Feng, et al.
Frontiers in Sustainable Food Systems (2023) Vol. 7
Open Access | Times Cited: 22
Internet of Things enabled open source assisted real-time blood glucose monitoring framework
Abubeker K. M, Ravi S. Ramani, Raja Krishnamoorthy, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 10
Enhancing Non-Invasive Blood Glucose Prediction from Photoplethysmography Signals via Heart Rate Variability-Based Features Selection Using Metaheuristic Algorithms
Saifeddin Alghlayini, Mohammed Azmi Al‐Betar, Mohamed Atef
Algorithms (2025) Vol. 18, Iss. 2, pp. 95-95
Open Access | Times Cited: 1
Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal
Ernia Susana, Kalamullah Ramli, Hendri Murfi, et al.
Information (2022) Vol. 13, Iss. 2, pp. 59-59
Open Access | Times Cited: 35
Recent Developments and Future Directions of Wearable Skin Biosignal Sensors
Dohyung Kim, JinKi Min, Seung Hwan Ko
Advanced Sensor Research (2023) Vol. 3, Iss. 2
Open Access | Times Cited: 19
Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review
Serena Zanelli, Mehdi Ammi, Magid Hallab, et al.
Sensors (2022) Vol. 22, Iss. 13, pp. 4890-4890
Open Access | Times Cited: 23
Non-Invasive Classification of Blood Glucose Level Based on Photoplethysmography Using Time–Frequency Analysis
Ernia Susana, Kalamullah Ramli, Prima Dewi Purnamasari, et al.
Information (2023) Vol. 14, Iss. 3, pp. 145-145
Open Access | Times Cited: 13
MMG-net: Multi modal approach to estimate blood glucose using multi-stream and cross modality attention
Moajjem Hossain Chowdhury, Muhammad E. H. Chowdhury, Abdulrahman Alqahtani
Biomedical Signal Processing and Control (2024) Vol. 92, pp. 105975-105975
Closed Access | Times Cited: 5
Non-invasive blood glucose estimation using a novel white-box model: An interpretable machine learning approach
Virendra Kumar, Abhay Divekar, Shraddha Habbu, et al.
Biomedical Signal Processing and Control (2025) Vol. 105, pp. 107520-107520
Closed Access
Noninvasive estimation of blood glucose and HbA1c using Quantum Machine Learning technique
Parama Sridevi, Masud Rabbani, Md Hasanul Aziz, et al.
Machine Learning with Applications (2025), pp. 100626-100626
Open Access
Accurate Estimation of Methemoglobin and Oxygen Saturation in Skin Tissue Using Diffuse Reflectance Spectroscopy and Artificial Intelligence
Isra Sahli, Wesam Bachir, Moustafa Sayem El‐Daher
Journal of Biophotonics (2025)
Open Access
Infrared absorption spectroscopy-based non-invasive blood glucose monitoring technology: A comprehensive review
T.J. Li, Qing Wang, Ying An, et al.
Biomedical Signal Processing and Control (2025) Vol. 106, pp. 107750-107750
Open Access
A Deep Sparse Capsule Network for Non-Invasive Blood Glucose Level Estimation Using a PPG Sensor
Narmatha Chellamani, Saleh Albelwi, Manimurugan Shanmuganathan, et al.
Sensors (2025) Vol. 25, Iss. 6, pp. 1868-1868
Open Access
Using Machine learning to predict blood glucose level based on Photoplethysmography
Shengchao Jian, Yu-Lung Lo, Yun-Tzu Chuang, et al.
Measurement (2025), pp. 117421-117421
Closed Access
Tiny-PPG: A lightweight deep neural network for real-time detection of motion artifacts in photoplethysmogram signals on edge devices
Yali Zheng, Chen Wu, Peizheng Cai, et al.
Internet of Things (2023) Vol. 25, pp. 101007-101007
Open Access | Times Cited: 12
Noninvasive Glucose Sensing In Vivo
Ho Man Colman Leung, Gregory P. Forlenza, Temiloluwa Prioleau, et al.
Sensors (2023) Vol. 23, Iss. 16, pp. 7057-7057
Open Access | Times Cited: 11
Clinical evaluation of a polarization-based optical noninvasive glucose sensing system
Ho Man Colman Leung, Chengyue Gong, Luke Geiser, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Personalized diabetes monitoring platform leveraging IoMT and AI for non-invasive estimation
P. Durga, Rahul Krishnan Pathinarupothi, K. A. Unnikrishna Menon, et al.
Smart Health (2023) Vol. 30, pp. 100428-100428
Closed Access | Times Cited: 10
EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
Shama Satter, Mrinmoy Sarker Turja, Tae-Ho Kwon, et al.
Applied Sciences (2024) Vol. 14, Iss. 4, pp. 1406-1406
Open Access | Times Cited: 3
Cuffless Blood Pressure Estimation Based on Monte Carlo Simulation Using Photoplethysmography Signals
Chowdhury Azimul Haque, Tae-Ho Kwon, Ki‐Doo Kim
Sensors (2022) Vol. 22, Iss. 3, pp. 1175-1175
Open Access | Times Cited: 13
Noninvasive Method for Measuring the Blood Glucose Level Using a Narrow Band Microstrip Antenna
Ayman R. Megdad, Rabah W. Aldhaheri, Nebras Sobahi
The Applied Computational Electromagnetics Society Journal (ACES) (2023)
Closed Access | Times Cited: 7
QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning
Md Nazmul Islam Shuzan, Moajjem Hossain Chowdhury, Muhammad E. H. Chowdhury, et al.
IEEE Access (2024) Vol. 12, pp. 77774-77790
Open Access | Times Cited: 2
Highly Sensitive Perovskite Photoplethysmography Sensor for Blood Glucose Sensing Using Machine Learning Techniques
Yongjian Zheng, Zhenye Zhan, Qiulan Chen, et al.
Advanced Science (2024) Vol. 11, Iss. 43
Open Access | Times Cited: 2
Machine-Learning-Based Noninvasive In Vivo Estimation of HbA1c Using Photoplethysmography Signals
Tae-Ho Kwon, Ki‐Doo Kim
Sensors (2022) Vol. 22, Iss. 8, pp. 2963-2963
Open Access | Times Cited: 12
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