A Review of Non-Invasive Blood Glucose Monitoring Using Near-Infrared NIR Spectroscopy - Technologies and Challenges
Main Article Content
Abstract
Diabetes mellitus is rapidly increasing worldwide, creating an urgent demand for continuous, accurate, and user-friendly glucose monitoring methods. Conventional invasive self-monitoring of blood glucose (SMBG), although widely adopted, still presents several limitations, including pain, inconvenience, high consumable costs, and the inability to support continuous monitoring. In this context, non-invasive glucose monitoring technologies based on near-infrared (NIR) spectroscopy have attracted significant attention from the research community. The operating principle relies on the absorption and scattering of NIR light interacting with characteristic chemical bonds of glucose molecules in biological tissues. This review presents the theoretical foundations of NIR spectroscopy, including transmission and reflectance sensor configurations, as well as emerging trends in integrating NIR photoplethysmography (PPG). Furthermore, the roles of signal processing techniques, nonlinear feature extraction, and machine learning models in modeling the relationship between optical signals and glucose concentration are comprehensively analyzed. Alongside its promising application potential, this paper also discusses remaining challenges such as motion artifacts, inter-individual physiological variability, and the requirement for personalized calibration.
Keywords
Machine learning, Near-infrared spectroscopy, Non-invasive glucose monitoring, Optical sensing, Photoplethysmography
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
[1]. T. Li, Q. Wang, Y. An, L. J. Guo, L. Ren, L. Li, and X. Chen, Infrared absorption spectroscopy-based non-invasive blood glucose monitoring technology: A comprehensive review, Biomed. Signal Process. Control, vol. 106, p. 107750, 2025, doi: 10.1016/j.bspc.2025.107750.
[2]. R. Jokari, Z. F. Mahyari, M. J. Moulodi, S. M. F. Ghiri, H. Tajalizadeh, A. L. Jahromi, A. Nakhostin, G. Abdollahifard, and H. Parsaei, An infrared non-invasive system for measuring blood glucose: A primary study using serum samples, J. Biomed. Phys. Eng., 2024, doi: 10.31661/jbpe.v0i0.2305-1618.
[3]. R. Pandey, N. C. Dingari, N. Spegazzini, R. R. Dasari, G. L. Horowitz, and I. Barman, Emerging trends in optical sensing of glycemic markers for diabetes monitoring, TrAC Trends Anal. Chem., vol. 64, pp. 100–107, 2014, doi: 10.1016/j.trac.2014.09.005.
[4]. A. Zilgarayeva, N. Smailov, S. Pavlov, S. Mirzakulova, M. Alimova, B. Kulambayev, and D. Nurpeissova, Optical sensor to improve the accuracy of non-invasive blood sugar monitoring, Indones. J. Electr. Eng. Comput. Sci., vol. 34, no. 3, pp. 1489–1498, 2024, doi: 10.11591/ijeecs.v34.i3.pp1489-1498.
[5]. D. D. Filippo, F. N. Sunstrum, J. U. Khan, and A. Welsh, Non-invasive glucose sensing technologies and products: A comprehensive review for researchers and clinicians, Sensors, vol. 23, no. 22, p. 9130, 2023, doi: 10.3390/s23229130.
[6]. I. M. A. Rahim, H. A. Rahim, R. Ghazali, R. Ismail, and J. Omar, Glucose detection in blood using near-infrared spectroscopy: Significant wavelength for glucose detection, J. Teknol., vol. 78, 2016, doi: 10.11113/jt.v78.9424.
[7]. M. F. A. M. Yunos and A. N. Nordin, Non-invasive glucose monitoring devices: A review, Bull. Electr. Eng. Inform., vol. 9, no. 6, pp. 2609–2618, 2020, doi: 10.11591/eei.v9i6.2628.
[8]. M. A. Al-Dhaheri, N. Mekkakia-Maaza, H. Mouhadjer, and A. Lakhdari, Noninvasive blood glucose monitoring system based on near-infrared method, Int. J. Electr. Comput. Eng., vol. 10, no. 2, pp. 1736–1746, 2020, doi: 10.11591/ijece.v10i2.pp1736-1746.
[9]. D. Piao, J. F. O’Hara, S. Bukkapatnam, and S. Ekin, Towards non-contact glucose sensing in aqueous turbid medium at ~1.1 meters distance, arXiv, 2022, doi: 10.48550/arxiv.2009.01208.
[10]. H. Jiang, T. Yao, and C. Ding, PPG-based glucose sensors: A review, Artif. Intell. Rev., vol. 58, no. 12, 2025, doi: 10.1007/s10462-025-11379-4.
[11]. A. Ahmed, S. Aziz, A. Abd-Alrazaq, F. Farooq, and J. I. Sheikh, Overview of artificial intelligence–driven wearable devices for diabetes: Scoping review, J. Med. Internet Res., vol. 24, no. 8, 2022, doi: 10.2196/36010.
[12]. L. A. Castro-Pimentel, A. del C. Téllez-Anguiano, O. I. Coronado-Reyes, and J. L. Díaz-Huerta, Three-wavelength PPG and support vector machine for non-invasive estimation of blood glucose, Res. Square, 2023, doi: 10.21203/rs.3.rs-2712243/v1.
[13]. M. Klimek and T. Tulwin, Continuous glucose monitoring: Review of promising technologies, MATEC Web Conf., vol. 252, p. 02012, 2019, doi: 10.1051/matecconf/201925202012.
[14]. D. K. Aristarkus, The long-term complications of hyperglycemia in both type 1 and type 2 diabetic patients, MOJ Proteomics Bioinform., vol. 7, no. 5, 2018, doi: 10.15406/mojpb.2018.07.00244.
[15]. H. Jeon, H. S. Kim, E. Chung, and D. Y. Lee, Nanozyme-based colorimetric biosensor with a systemic quantification algorithm for noninvasive glucose monitoring, Theranostics, vol. 12, no. 14, pp. 6308–6320, 2022, doi: 10.7150/thno.72152.
[16]. I. L. Jernelv, K. Mileńko, S. S. Fuglerud, D. R. Hjelme, R. Ellingsen, and A. Aksnes, A review of optical methods for continuous glucose monitoring, Appl. Spectrosc. Rev., vol. 54, no. 7, pp. 543–572, 2018, doi: 10.1080/05704928.2018.1486324.
[17]. E. A. Belfarsi, H. Flores, and M. Valero, Reliable noninvasive glucose sensing via CNN-based spectroscopy, arXiv, 2025, doi: 10.48550/arxiv.2506.13819.
[18]. S. Laha, A. Rajput, S. S. Laha, and R. Jadhav, A concise and systematic review on non-invasive glucose monitoring for potential diabetes management, Biosensors, vol. 12, no. 11, p. 965, 2022, doi: 10.3390/bios12110965.
[19]. A. Li, X. Li, Y. Xu, C. Wu, Z. Geng, J. Zhang, X. Wang, Y. Li, H. Li, X. Guo, and F. Tang, Evaluating the clinical accuracy of a non-invasive single-fasting-calibration glucometer in patients with diabetes: A multicentre study, Diabetes Ther., vol. 14, no. 6, pp. 989–1004, 2023, doi: 10.1007/s13300-023-01402-8.
[20]. J. Chen and K.-I. Lai, Cost-effective noninvasive 2.4 GHz microwave blood glucose sensor, Sens. Mater., vol. 36, no. 5, pp. 1905–1916, 2024, doi: 10.18494/sam4626.
[21]. J. Shi, R. Fernández-García, and I. Gil, Sensor technologies for non-invasive blood glucose monitoring, Sensors, vol. 25, no. 12, p. 3591, 2025, doi: 10.3390/s25123591.
[22]. J. Al-Nabulsi, H. A. Owida, J. Ma’touq, S. Matar, E. Al-Aazeh, A. Al-Maaiouf, and A. Bleibel, Non-invasive sensing techniques for glucose detection: A review, Bull. Electr. Eng. Inform., vol. 11, no. 4, pp. 1926–1938, 2022, doi: 10.11591/eei.v11i4.3584.
[23]. H. Zhang, Application of biosensors in non-invasive blood glucose monitoring, E3S Web Conf., vol. 553, p. 05001, 2024, doi: 10.1051/e3sconf/202455305001.
[24]. A. Hina and W. Saadeh, Noninvasive blood glucose monitoring systems using near-infrared technology: A review, Sensors, vol. 22, no. 13, p. 4855, 2022, doi: 10.3390/s22134855.
[25]. S. A. Pullano, M. Greco, M. G. Bianco, D. Foti, A. Brunetti, and A. S. Fiorillo, Glucose biosensors in clinical practice: Principles, limits and perspectives of currently used devices, Theranostics, vol. 12, no. 2, pp. 493–511, 2021, doi: 10.7150/thno.64035.
[26]. O. Abdalsalam and A. A. Awouda, Non-invasive glucose monitoring using scattering spectroscopy, Am. J. Biomed. Eng., vol. 4, no. 3, pp. 53–59, 2014.
[27]. S. S. Fuglerud, R. Ellingsen, A. Aksnes, and D. R. Hjelme, Investigation of the effect of clinically relevant interferents on glucose monitoring using near-infrared spectroscopy, J. Biophotonics, vol. 14, no. 5, 2021, doi: 10.1002/jbio.202000450.
[28]. A. M. Joshi, P. Jain, and S. P. Mohanty, Everything you wanted to know about continuous glucose monitoring, IEEE Consum. Electron. Mag., vol. 10, no. 6, pp. 61–70, 2021, doi: 10.1109/MCE.2021.3073498.
[29]. S. Jang, Review of emerging approaches in non- or minimally invasive glucose monitoring and their application to physiological human body fluids, Int. J. Biosens. Bioelectron., vol. 4, no. 2, 2018, doi: 10.15406/ijbsbe.2018.04.00087.
[30]. P. Jain, A. M. Joshi, and S. P. Mohanty, iGLU: An intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare, IEEE Consum. Electron. Mag., vol. 9, no. 1, pp. 35–42, 2019, doi: 10.1109/MCE.2019.2940855.
[31]. A. Li, C. Yao, J. Xia, H. Wang, Q. Cheng, R. V. Penty, Y. Fainman, and S. Pan, Advances in cost-effective integrated spectrometers, Light Sci. Appl., vol. 11, no. 1, 2022, doi: 10.1038/s41377-022-00853-1.
[32]. J. Tenhunen, H. Kopola, and R. Myllylä, Non-invasive glucose measurement based on selective near infrared absorption: Requirements on instrumentation and spectral range, Meas., vol. 24, no. 3, pp. 173–182, 1998, doi: 10.1016/S0263-2241(98)00054-2.
[33]. B. Todaro, F. Begarani, F. Sartori, and S. Luin, Is Raman the best strategy towards the development of non-invasive continuous glucose monitoring devices for diabetes management?, Front. Chem., vol. 10, 2022, doi: 10.3389/fchem.2022.994272.
[34]. W. Maentele, M. Kaluza, S. Janik, T. Lubinski, M. Saita, P. J. Lachmann, L. Canini, and V. Lepro, Clinical validation of non-invasive blood glucose measurements by mid-infrared spectroscopy, Res. Square, 2024, doi: 10.21203/rs.3.rs-5289491/v1.
[35]. S. P. Nichols, A. Koh, W. L. Storm, J. H. Shin, and M. H. Schoenfisch, Biocompatible materials for continuous glucose monitoring devices, Carolina Digit. Repos., 2013, doi: 10.17615/wx8m-wn05.
[36]. I. L. Jernelv, K. Mileńko, S. S. Fuglerud, D. R. Hjelme, R. Ellingsen, and A. Aksnes, A review of optical methods for continuous glucose monitoring, Appl. Spectrosc. Rev., vol. 54, no. 7, pp. 543–572, 2018, doi: 10.1080/05704928.2018.1486324.
[37]. A. K. M., R. S. Ramani, R. Krishnamoorthy, S. Gogula, S. Baskar, S. Muthu, G. Chellamuthu, and K. Subramaniam, Internet of Things enabled open source assisted real-time blood glucose monitoring framework, Sci. Rep., vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-56677-z.
[38]. P. Daarani and Kavithamani, Blood glucose level monitoring by noninvasive method using near infra red sensor, Int. J. Latest Trends Eng. Technol., 2017, doi: 10.21172/1.ires.19.
[39]. H. Khadem, H. Nemat, D. Elliott, and M. Benaissa, Signal fragmentation based feature vector generation in a model agnostic framework with application to glucose quantification using absorption spectroscopy, SSRN Electron. J., 2022, doi: 10.2139/ssrn.4003278.
[40]. A. J. L. Martins et al., A comprehensive review of non-invasive optical and microwave biosensors for glucose monitoring, Biosens. Bioelectron., vol. 271, p. 117081, 2024, doi: 10.1016/j.bios.2024.117081.
[41]. K. V. Pozhar, M. O. Mikhailov, E. L. Litinskaia, and E. A. Polyakova, Near-infrared spectroscopy for noninvasive measurement of blood glucose: Problems, progress, tasks, Biomed. Eng., vol. 56, no. 1, pp. 64–72, 2022, doi: 10.1007/s10527-022-10168-5.
[42]. S. Rȧjeswari and V. Ponnusamy, Development of sensor system and data analytic framework for non-invasive blood glucose prediction, Sci. Rep., vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-59744-7.
[43]. H. Manoharan, D. Jayaseelan, and S. Appu, A comparative study on continuous glucose monitoring devices for managing diabetes mellitus, Rev. Intell. Artif., vol. 37, no. 5, pp. 1351–1360, 2023, doi: 10.18280/ria.370528.
[44]. J. Yadav, A. Rani, V. Singh, and B. M. Murari, Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy, Biomed. Signal Process. Control, vol. 18, pp. 214–227, 2015, doi: 10.1016/j.bspc.2015.01.005.
[45]. M. Naresh, V. Nagaraju, S. Kollem, J. Kumar, and S. Peddakrishna, Non-invasive glucose prediction and classification using NIR technology with machine learning, Heliyon, vol. 10, no. 7, 2024, doi: 10.1016/j.heliyon.2024.e28720.
[46]. M. Zeynali, K. Alipour, B. Tarvirdizadeh, and M. Ghamari, Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML, Sci. Rep., vol. 15, no. 1, p. 581, 2025, doi: 10.1038/s41598-024-84265-8.
[47]. E. Vargová, A. Němcová, and Z. Nováková, Non-invasive PPG-based estimation of blood glucose level, Lékař a Technika, vol. 19, 2023, doi: 10.14311/ctj.2023.1.04.
[48]. T. T. Islam, M. Ahmed, M. Hassanuzzaman, S. A. B. Amir, and T. Rahman, Blood glucose level regression for smartphone PPG signals using machine learning, Appl. Sci., vol. 11, no. 2, p. 618, 2021, doi: 10.3390/app11020618.
[49]. F. Handayani, A. Andang, and F. M. S. Nursuwars, Improvement of photoplethysmography signal quality via multi-stage preprocessing, Maj. Ilm. Teknol. Elektro, vol. 24, no. 1, pp. 61–70, 2025, doi: 10.24843/mite.205.v24i01.p06.
[50]. M. Essalat, M. B. Mashhadi, and F. Marvasti, Supervised heart rate tracking using wrist-type photoplethysmographic (PPG) signals during physical exercise without simultaneous acceleration signals, in Proc. IEEE GlobalSIP, 2016, pp. 1166–1170, doi: 10.1109/GlobalSIP.2016.7906025.
[51]. A. Sološenko, B. Paliakaitė, V. Marozas, and L. Sörnmo, Training convolutional neural networks on simulated photoplethysmography data: Application to bradycardia and tachycardia detection, Front. Physiol., vol. 13, 2022, doi: 10.3389/fphys.2022.928098.
[52]. V. C. Bavkar and A. A. Shinde, Machine learning algorithms for diabetes prediction and neural network method for blood glucose measurement, Indian J. Sci. Technol., vol. 14, no. 10, pp. 869–875, 2021, doi: 10.17485/ijst/v14i10.2187.
[53]. J. Chu, W.-T. Yang, W. Lu, Y.-T. Chang, T. Hsieh, and F.-L. Yang, Photoplethysmography-based non-invasive blood glucose prediction by deep learning with cohort arrangement and quarterly measured HbA1c, Sensors, vol. 21, no. 23, p. 7815, 2021, doi: 10.3390/s21237815.
[54]. G. Adıgüzel, Ü. Şentürk, and K. Polat, Blood glucose level estimation using photoplethysmography (PPG) signals with explainable artificial intelligence techniques, Open J. Nano, vol. 9, no. 1, pp. 45–55, 2024, doi: 10.56171/ojn.1473276.
[55]. A. Ahmed, S. Aziz, A. Abd-Alrazaq, F. Farooq, M. Househ, and J. I. Sheikh, The effectiveness of wearable devices using artificial intelligence for blood glucose level forecasting or prediction: Systematic review, J. Med. Internet Res., vol. 25, 2023, doi: 10.2196/40259.
[56]. D. Klyve, K. Pandya, C. Ward, and B. Shelton, Novel data preprocessing techniques in an expanded dataset improve machine learning model accuracy for a non-invasive blood glucose monitor, medRxiv, 2023, doi: 10.1101/2023.07.24.23293113.
[57]. J. He, H. Su, X. Xiong, X. Yang, Y. Cai, and Y. Xue, Exploring the potential of deep learning models integrating Transformer and LSTM in predicting blood glucose levels for T1D patients, Res. Square, 2024, doi: 10.21203/rs.3.rs-4440333/v1.
[58]. Z. He, H. Zhang, X. Chen, J. Shi, L. Bai, Z. Fang, and R. Wang, Hemorrhagic risk prediction in coronary artery disease patients based on photoplethysmography and machine learning, Sci. Rep., vol. 12, no. 1, 2022, doi: 10.1038/s41598-022-22719-7.
[59]. C.-N. Lin, C.-P. Chang, J. Lin, J. Chang, Y.-J. Hung, and P. Ko, Clinical validation of comprehensive time- and frequency-domain photoplethysmography features from a single-sensor system for non-invasive assessment of vascular load and systolic blood pressure, Front. Physiol., vol. 16, 2025, doi: 10.3389/fphys.2025.1695391.
[60]. F. Smarandache, S. Akula, S. I. Alzahrani, F. Arslan, and A. Ijaz, PPG-based sleep stage classification using pulse wave feature fusion and explainable AI, Eng. Technol. Appl. Sci. Res., vol. 15, no. 5, pp. 27640–27647, 2025, doi: 10.48084/etasr.13077.
[61]. Y. Liang, Z. Chen, G. Liu, and M. Elgendi, A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China, Sci. Data, vol. 5, 2018, doi: 10.1038/sdata.2018.20.
[62]. P. Charlton et al., Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: A review from VascAgeNet, Am. J. Physiol. Heart Circ. Physiol., vol. 322, no. 4, 2021, doi: 10.1152/ajpheart.00392.2021.
[63]. F. Shoeibi, E. Najafiaghdam, and A. Ebrahimi, Poincaré’s section analysis of photoplethysmography signals for cuff-less non-invasive blood pressure measurement, Res. Square, 2021, doi: 10.21203/rs.3.rs-171469/v1.
[64]. Y. A. Djawad, A. Mu’nisa, P. Rusung, A. Kurniawan, I. S. Idris, and M. Taiyeb, Essential feature extraction of photoplethysmography signal of men and women in their 20s, Eng. J., vol. 21, no. 4, pp. 259–268, 2017, doi: 10.4186/ej.2017.21.4.259.
[65]. S. Iqbal, J. Bacardit, B. Griffiths, and J. Allen, Deep learning classification of systemic sclerosis from multi-site photoplethysmography signals, Front. Physiol., vol. 14, 2023, doi: 10.3389/fphys.2023.1242807.
[66]. F. S. Köklükaya and M. Öztürk, Blood pressure and heart rate estimation via TQWT-based decomposition of PPG signals, 2023.
[67]. R. Maccay and R. Weerasekera, Machine learning assisted postural movement recognition using photoplethysmography (PPG), arXiv, 2024, doi: 10.48550/arxiv.2411.11862.
[68]. X. Hu et al., Blood pressure stratification using photoplethysmography and light gradient boosting machine, Front. Physiol., vol. 14, 2023, doi: 10.3389/fphys.2023.1072273.
[69]. S. Abdullah and A. Kristoffersson, Machine learning approaches for cardiovascular hypertension stage estimation using photoplethysmography and clinical features, Front. Cardiovasc. Med., vol. 10, 2023, doi: 10.3389/fcvm.2023.1285066.
[70]. G. Frederick, T. Yaswant, and B. T. A., PPG signals for hypertension diagnosis: A novel method using deep learning models, arXiv, 2023, doi: 10.48550/arxiv.2304.06952.
[71]. Z. Papalamprakopoulou, D. N. Stavropoulos, S. Moustakidis, D. V. Avgerinos, M. Efremidis, and P. N. Kampaktsis, Artificial intelligence-enabled atrial fibrillation detection using smartwatches: Current status and future perspectives, Front. Cardiovasc. Med., vol. 11, 2024, doi: 10.3389/fcvm.2024.1432876.
[72]. D. Seok, S.-H. Lee, M. Kim, J. Cho, and C. Kim, Motion artifact removal techniques for wearable EEG and PPG sensor systems, Front. Electron., vol. 2, 2021, doi: 10.3389/felec.2021.685513.
[73]. C. Ding, R. Xiao, W. Wang, E. Holdsworth, and X. Hu, Photoplethysmography-based atrial fibrillation detection: An updated review from July 2019, arXiv, 2023, doi: 10.48550/arxiv.2310.14155.
[74]. P. Karimpour, J. M. May, and P. A. Kyriacou, Photoplethysmography for the assessment of arterial stiffness, Sensors, vol. 23, no. 24, p. 9882, 2023, doi: 10.3390/s23249882.
[75]. G. Nie et al., A review of deep learning methods for photoplethysmography data, arXiv, 2024, doi: 10.48550/arxiv.2401.12783.
[76]. N. Nasir, M. Sameer, F. Barneih, O. Alshaltone, and M. Ahmed, Deep learning classification of photoplethysmogram signal for hypertension levels, arXiv, 2024, doi: 10.48550/arxiv.2405.14556.
[77]. S. Kwon et al., Deep learning approaches to detect atrial fibrillation using photoplethysmographic signals: Algorithms development study, JMIR Mhealth Uhealth, vol. 7, no. 6, 2019, doi: 10.2196/12770.
[78]. E. Lan, HDformer: A higher-dimensional transformer for detecting diabetes utilizing long-range vascular signals, in Proc. AAAI Conf. Artif. Intell., vol. 38, no. 12, pp. 13320–13328, 2024, doi: 10.1609/aaai.v38i12.29233.
[79]. C.-C. Huang et al., Examining arterial pulsation to identify and risk-stratify heart failure subjects with deep neural network, Phys. Eng. Sci. Med., vol. 47, no. 2, pp. 477–490, 2024, doi: 10.1007/s13246-023-01378-6.
[80]. A. Qtaishat, W. Suryani, and W. S. W. Awang, Systematic review: Opportunities and challenges of machine learning techniques for cardiovascular disease prediction, J. Southwest Jiaotong Univ., vol. 59, no. 2, 2024, doi: 10.35741/issn.0258-2724.59.2.8.
[81]. X. Wen et al., Clinlabomics: Leveraging clinical laboratory data by data mining strategies, BMC Bioinformatics, vol. 23, no. 1, 2022, doi: 10.1186/s12859-022-04926-1.
[82]. H. H. Jung, H. Lee, J. Yea, and K. Jang, Wearable electrochemical sensors for real-time monitoring in diabetes mellitus and associated complications, Soft Sci., vol. 4, no. 2, 2024, doi: 10.20517/ss.2024.02.
[83]. T. Wang, W. Li, and D. M. Lewis, Blood glucose forecasting using LSTM variants under the context of open source artificial pancreas system, in Proc. Hawaii Int. Conf. Syst. Sci., 2020, doi: 10.24251/hicss.2020.397.
[84]. H. P. Tripathy, P. Pattanaik, D. K. Mishra, S. K. Kamilla, and W. Holderbaum, Experimental and probabilistic model validation of ultrasonic MEMS transceiver for blood glucose sensing, Sci. Rep., vol. 12, no. 1, 2022, doi: 10.1038/s41598-022-25717-x.
[85]. K. Liu et al., Machine learning models for blood glucose level prediction in patients with diabetes mellitus: Systematic review and network meta-analysis, JMIR Med. Inform., vol. 11, 2023, doi: 10.2196/47833.
[86]. A. Soliman, A. M. Nor, O. Fratu, S. Halunga, O. A. Omer, and A. S. Mubark, Non-invasive glucose level monitoring from PPG using a hybrid CNN-GRU deep learning network, arXiv, 2024, doi: 10.48550/arxiv.2411.11094.
[87]. S. Kriventsov, A. Lindsey, and A. Hayeri, The Diabits app for smartphone-assisted predictive monitoring of glycemia in patients with diabetes: Retrospective observational study, JMIR Diabetes, vol. 5, no. 3, 2020, doi: 10.2196/18660.
[88]. G. Frontino et al., Future perspectives in glucose monitoring sensors, US Endocrinol., vol. 9, no. 1, pp. 21–27, 2013, doi: 10.17925/use.2013.09.01.21.
[89]. A. Singhal and P. Singhal, Analysis of non-invasive devices and techniques involved in continuous monitoring of interstitial glucose of patients with type 1 diabetes, Int. J. Res. Appl. Sci. Eng. Technol., vol. 11, no. 12, pp. 1993–2002, 2023, doi: 10.22214/ijraset.2023.57784.
[2]. R. Jokari, Z. F. Mahyari, M. J. Moulodi, S. M. F. Ghiri, H. Tajalizadeh, A. L. Jahromi, A. Nakhostin, G. Abdollahifard, and H. Parsaei, An infrared non-invasive system for measuring blood glucose: A primary study using serum samples, J. Biomed. Phys. Eng., 2024, doi: 10.31661/jbpe.v0i0.2305-1618.
[3]. R. Pandey, N. C. Dingari, N. Spegazzini, R. R. Dasari, G. L. Horowitz, and I. Barman, Emerging trends in optical sensing of glycemic markers for diabetes monitoring, TrAC Trends Anal. Chem., vol. 64, pp. 100–107, 2014, doi: 10.1016/j.trac.2014.09.005.
[4]. A. Zilgarayeva, N. Smailov, S. Pavlov, S. Mirzakulova, M. Alimova, B. Kulambayev, and D. Nurpeissova, Optical sensor to improve the accuracy of non-invasive blood sugar monitoring, Indones. J. Electr. Eng. Comput. Sci., vol. 34, no. 3, pp. 1489–1498, 2024, doi: 10.11591/ijeecs.v34.i3.pp1489-1498.
[5]. D. D. Filippo, F. N. Sunstrum, J. U. Khan, and A. Welsh, Non-invasive glucose sensing technologies and products: A comprehensive review for researchers and clinicians, Sensors, vol. 23, no. 22, p. 9130, 2023, doi: 10.3390/s23229130.
[6]. I. M. A. Rahim, H. A. Rahim, R. Ghazali, R. Ismail, and J. Omar, Glucose detection in blood using near-infrared spectroscopy: Significant wavelength for glucose detection, J. Teknol., vol. 78, 2016, doi: 10.11113/jt.v78.9424.
[7]. M. F. A. M. Yunos and A. N. Nordin, Non-invasive glucose monitoring devices: A review, Bull. Electr. Eng. Inform., vol. 9, no. 6, pp. 2609–2618, 2020, doi: 10.11591/eei.v9i6.2628.
[8]. M. A. Al-Dhaheri, N. Mekkakia-Maaza, H. Mouhadjer, and A. Lakhdari, Noninvasive blood glucose monitoring system based on near-infrared method, Int. J. Electr. Comput. Eng., vol. 10, no. 2, pp. 1736–1746, 2020, doi: 10.11591/ijece.v10i2.pp1736-1746.
[9]. D. Piao, J. F. O’Hara, S. Bukkapatnam, and S. Ekin, Towards non-contact glucose sensing in aqueous turbid medium at ~1.1 meters distance, arXiv, 2022, doi: 10.48550/arxiv.2009.01208.
[10]. H. Jiang, T. Yao, and C. Ding, PPG-based glucose sensors: A review, Artif. Intell. Rev., vol. 58, no. 12, 2025, doi: 10.1007/s10462-025-11379-4.
[11]. A. Ahmed, S. Aziz, A. Abd-Alrazaq, F. Farooq, and J. I. Sheikh, Overview of artificial intelligence–driven wearable devices for diabetes: Scoping review, J. Med. Internet Res., vol. 24, no. 8, 2022, doi: 10.2196/36010.
[12]. L. A. Castro-Pimentel, A. del C. Téllez-Anguiano, O. I. Coronado-Reyes, and J. L. Díaz-Huerta, Three-wavelength PPG and support vector machine for non-invasive estimation of blood glucose, Res. Square, 2023, doi: 10.21203/rs.3.rs-2712243/v1.
[13]. M. Klimek and T. Tulwin, Continuous glucose monitoring: Review of promising technologies, MATEC Web Conf., vol. 252, p. 02012, 2019, doi: 10.1051/matecconf/201925202012.
[14]. D. K. Aristarkus, The long-term complications of hyperglycemia in both type 1 and type 2 diabetic patients, MOJ Proteomics Bioinform., vol. 7, no. 5, 2018, doi: 10.15406/mojpb.2018.07.00244.
[15]. H. Jeon, H. S. Kim, E. Chung, and D. Y. Lee, Nanozyme-based colorimetric biosensor with a systemic quantification algorithm for noninvasive glucose monitoring, Theranostics, vol. 12, no. 14, pp. 6308–6320, 2022, doi: 10.7150/thno.72152.
[16]. I. L. Jernelv, K. Mileńko, S. S. Fuglerud, D. R. Hjelme, R. Ellingsen, and A. Aksnes, A review of optical methods for continuous glucose monitoring, Appl. Spectrosc. Rev., vol. 54, no. 7, pp. 543–572, 2018, doi: 10.1080/05704928.2018.1486324.
[17]. E. A. Belfarsi, H. Flores, and M. Valero, Reliable noninvasive glucose sensing via CNN-based spectroscopy, arXiv, 2025, doi: 10.48550/arxiv.2506.13819.
[18]. S. Laha, A. Rajput, S. S. Laha, and R. Jadhav, A concise and systematic review on non-invasive glucose monitoring for potential diabetes management, Biosensors, vol. 12, no. 11, p. 965, 2022, doi: 10.3390/bios12110965.
[19]. A. Li, X. Li, Y. Xu, C. Wu, Z. Geng, J. Zhang, X. Wang, Y. Li, H. Li, X. Guo, and F. Tang, Evaluating the clinical accuracy of a non-invasive single-fasting-calibration glucometer in patients with diabetes: A multicentre study, Diabetes Ther., vol. 14, no. 6, pp. 989–1004, 2023, doi: 10.1007/s13300-023-01402-8.
[20]. J. Chen and K.-I. Lai, Cost-effective noninvasive 2.4 GHz microwave blood glucose sensor, Sens. Mater., vol. 36, no. 5, pp. 1905–1916, 2024, doi: 10.18494/sam4626.
[21]. J. Shi, R. Fernández-García, and I. Gil, Sensor technologies for non-invasive blood glucose monitoring, Sensors, vol. 25, no. 12, p. 3591, 2025, doi: 10.3390/s25123591.
[22]. J. Al-Nabulsi, H. A. Owida, J. Ma’touq, S. Matar, E. Al-Aazeh, A. Al-Maaiouf, and A. Bleibel, Non-invasive sensing techniques for glucose detection: A review, Bull. Electr. Eng. Inform., vol. 11, no. 4, pp. 1926–1938, 2022, doi: 10.11591/eei.v11i4.3584.
[23]. H. Zhang, Application of biosensors in non-invasive blood glucose monitoring, E3S Web Conf., vol. 553, p. 05001, 2024, doi: 10.1051/e3sconf/202455305001.
[24]. A. Hina and W. Saadeh, Noninvasive blood glucose monitoring systems using near-infrared technology: A review, Sensors, vol. 22, no. 13, p. 4855, 2022, doi: 10.3390/s22134855.
[25]. S. A. Pullano, M. Greco, M. G. Bianco, D. Foti, A. Brunetti, and A. S. Fiorillo, Glucose biosensors in clinical practice: Principles, limits and perspectives of currently used devices, Theranostics, vol. 12, no. 2, pp. 493–511, 2021, doi: 10.7150/thno.64035.
[26]. O. Abdalsalam and A. A. Awouda, Non-invasive glucose monitoring using scattering spectroscopy, Am. J. Biomed. Eng., vol. 4, no. 3, pp. 53–59, 2014.
[27]. S. S. Fuglerud, R. Ellingsen, A. Aksnes, and D. R. Hjelme, Investigation of the effect of clinically relevant interferents on glucose monitoring using near-infrared spectroscopy, J. Biophotonics, vol. 14, no. 5, 2021, doi: 10.1002/jbio.202000450.
[28]. A. M. Joshi, P. Jain, and S. P. Mohanty, Everything you wanted to know about continuous glucose monitoring, IEEE Consum. Electron. Mag., vol. 10, no. 6, pp. 61–70, 2021, doi: 10.1109/MCE.2021.3073498.
[29]. S. Jang, Review of emerging approaches in non- or minimally invasive glucose monitoring and their application to physiological human body fluids, Int. J. Biosens. Bioelectron., vol. 4, no. 2, 2018, doi: 10.15406/ijbsbe.2018.04.00087.
[30]. P. Jain, A. M. Joshi, and S. P. Mohanty, iGLU: An intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare, IEEE Consum. Electron. Mag., vol. 9, no. 1, pp. 35–42, 2019, doi: 10.1109/MCE.2019.2940855.
[31]. A. Li, C. Yao, J. Xia, H. Wang, Q. Cheng, R. V. Penty, Y. Fainman, and S. Pan, Advances in cost-effective integrated spectrometers, Light Sci. Appl., vol. 11, no. 1, 2022, doi: 10.1038/s41377-022-00853-1.
[32]. J. Tenhunen, H. Kopola, and R. Myllylä, Non-invasive glucose measurement based on selective near infrared absorption: Requirements on instrumentation and spectral range, Meas., vol. 24, no. 3, pp. 173–182, 1998, doi: 10.1016/S0263-2241(98)00054-2.
[33]. B. Todaro, F. Begarani, F. Sartori, and S. Luin, Is Raman the best strategy towards the development of non-invasive continuous glucose monitoring devices for diabetes management?, Front. Chem., vol. 10, 2022, doi: 10.3389/fchem.2022.994272.
[34]. W. Maentele, M. Kaluza, S. Janik, T. Lubinski, M. Saita, P. J. Lachmann, L. Canini, and V. Lepro, Clinical validation of non-invasive blood glucose measurements by mid-infrared spectroscopy, Res. Square, 2024, doi: 10.21203/rs.3.rs-5289491/v1.
[35]. S. P. Nichols, A. Koh, W. L. Storm, J. H. Shin, and M. H. Schoenfisch, Biocompatible materials for continuous glucose monitoring devices, Carolina Digit. Repos., 2013, doi: 10.17615/wx8m-wn05.
[36]. I. L. Jernelv, K. Mileńko, S. S. Fuglerud, D. R. Hjelme, R. Ellingsen, and A. Aksnes, A review of optical methods for continuous glucose monitoring, Appl. Spectrosc. Rev., vol. 54, no. 7, pp. 543–572, 2018, doi: 10.1080/05704928.2018.1486324.
[37]. A. K. M., R. S. Ramani, R. Krishnamoorthy, S. Gogula, S. Baskar, S. Muthu, G. Chellamuthu, and K. Subramaniam, Internet of Things enabled open source assisted real-time blood glucose monitoring framework, Sci. Rep., vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-56677-z.
[38]. P. Daarani and Kavithamani, Blood glucose level monitoring by noninvasive method using near infra red sensor, Int. J. Latest Trends Eng. Technol., 2017, doi: 10.21172/1.ires.19.
[39]. H. Khadem, H. Nemat, D. Elliott, and M. Benaissa, Signal fragmentation based feature vector generation in a model agnostic framework with application to glucose quantification using absorption spectroscopy, SSRN Electron. J., 2022, doi: 10.2139/ssrn.4003278.
[40]. A. J. L. Martins et al., A comprehensive review of non-invasive optical and microwave biosensors for glucose monitoring, Biosens. Bioelectron., vol. 271, p. 117081, 2024, doi: 10.1016/j.bios.2024.117081.
[41]. K. V. Pozhar, M. O. Mikhailov, E. L. Litinskaia, and E. A. Polyakova, Near-infrared spectroscopy for noninvasive measurement of blood glucose: Problems, progress, tasks, Biomed. Eng., vol. 56, no. 1, pp. 64–72, 2022, doi: 10.1007/s10527-022-10168-5.
[42]. S. Rȧjeswari and V. Ponnusamy, Development of sensor system and data analytic framework for non-invasive blood glucose prediction, Sci. Rep., vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-59744-7.
[43]. H. Manoharan, D. Jayaseelan, and S. Appu, A comparative study on continuous glucose monitoring devices for managing diabetes mellitus, Rev. Intell. Artif., vol. 37, no. 5, pp. 1351–1360, 2023, doi: 10.18280/ria.370528.
[44]. J. Yadav, A. Rani, V. Singh, and B. M. Murari, Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy, Biomed. Signal Process. Control, vol. 18, pp. 214–227, 2015, doi: 10.1016/j.bspc.2015.01.005.
[45]. M. Naresh, V. Nagaraju, S. Kollem, J. Kumar, and S. Peddakrishna, Non-invasive glucose prediction and classification using NIR technology with machine learning, Heliyon, vol. 10, no. 7, 2024, doi: 10.1016/j.heliyon.2024.e28720.
[46]. M. Zeynali, K. Alipour, B. Tarvirdizadeh, and M. Ghamari, Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML, Sci. Rep., vol. 15, no. 1, p. 581, 2025, doi: 10.1038/s41598-024-84265-8.
[47]. E. Vargová, A. Němcová, and Z. Nováková, Non-invasive PPG-based estimation of blood glucose level, Lékař a Technika, vol. 19, 2023, doi: 10.14311/ctj.2023.1.04.
[48]. T. T. Islam, M. Ahmed, M. Hassanuzzaman, S. A. B. Amir, and T. Rahman, Blood glucose level regression for smartphone PPG signals using machine learning, Appl. Sci., vol. 11, no. 2, p. 618, 2021, doi: 10.3390/app11020618.
[49]. F. Handayani, A. Andang, and F. M. S. Nursuwars, Improvement of photoplethysmography signal quality via multi-stage preprocessing, Maj. Ilm. Teknol. Elektro, vol. 24, no. 1, pp. 61–70, 2025, doi: 10.24843/mite.205.v24i01.p06.
[50]. M. Essalat, M. B. Mashhadi, and F. Marvasti, Supervised heart rate tracking using wrist-type photoplethysmographic (PPG) signals during physical exercise without simultaneous acceleration signals, in Proc. IEEE GlobalSIP, 2016, pp. 1166–1170, doi: 10.1109/GlobalSIP.2016.7906025.
[51]. A. Sološenko, B. Paliakaitė, V. Marozas, and L. Sörnmo, Training convolutional neural networks on simulated photoplethysmography data: Application to bradycardia and tachycardia detection, Front. Physiol., vol. 13, 2022, doi: 10.3389/fphys.2022.928098.
[52]. V. C. Bavkar and A. A. Shinde, Machine learning algorithms for diabetes prediction and neural network method for blood glucose measurement, Indian J. Sci. Technol., vol. 14, no. 10, pp. 869–875, 2021, doi: 10.17485/ijst/v14i10.2187.
[53]. J. Chu, W.-T. Yang, W. Lu, Y.-T. Chang, T. Hsieh, and F.-L. Yang, Photoplethysmography-based non-invasive blood glucose prediction by deep learning with cohort arrangement and quarterly measured HbA1c, Sensors, vol. 21, no. 23, p. 7815, 2021, doi: 10.3390/s21237815.
[54]. G. Adıgüzel, Ü. Şentürk, and K. Polat, Blood glucose level estimation using photoplethysmography (PPG) signals with explainable artificial intelligence techniques, Open J. Nano, vol. 9, no. 1, pp. 45–55, 2024, doi: 10.56171/ojn.1473276.
[55]. A. Ahmed, S. Aziz, A. Abd-Alrazaq, F. Farooq, M. Househ, and J. I. Sheikh, The effectiveness of wearable devices using artificial intelligence for blood glucose level forecasting or prediction: Systematic review, J. Med. Internet Res., vol. 25, 2023, doi: 10.2196/40259.
[56]. D. Klyve, K. Pandya, C. Ward, and B. Shelton, Novel data preprocessing techniques in an expanded dataset improve machine learning model accuracy for a non-invasive blood glucose monitor, medRxiv, 2023, doi: 10.1101/2023.07.24.23293113.
[57]. J. He, H. Su, X. Xiong, X. Yang, Y. Cai, and Y. Xue, Exploring the potential of deep learning models integrating Transformer and LSTM in predicting blood glucose levels for T1D patients, Res. Square, 2024, doi: 10.21203/rs.3.rs-4440333/v1.
[58]. Z. He, H. Zhang, X. Chen, J. Shi, L. Bai, Z. Fang, and R. Wang, Hemorrhagic risk prediction in coronary artery disease patients based on photoplethysmography and machine learning, Sci. Rep., vol. 12, no. 1, 2022, doi: 10.1038/s41598-022-22719-7.
[59]. C.-N. Lin, C.-P. Chang, J. Lin, J. Chang, Y.-J. Hung, and P. Ko, Clinical validation of comprehensive time- and frequency-domain photoplethysmography features from a single-sensor system for non-invasive assessment of vascular load and systolic blood pressure, Front. Physiol., vol. 16, 2025, doi: 10.3389/fphys.2025.1695391.
[60]. F. Smarandache, S. Akula, S. I. Alzahrani, F. Arslan, and A. Ijaz, PPG-based sleep stage classification using pulse wave feature fusion and explainable AI, Eng. Technol. Appl. Sci. Res., vol. 15, no. 5, pp. 27640–27647, 2025, doi: 10.48084/etasr.13077.
[61]. Y. Liang, Z. Chen, G. Liu, and M. Elgendi, A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China, Sci. Data, vol. 5, 2018, doi: 10.1038/sdata.2018.20.
[62]. P. Charlton et al., Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: A review from VascAgeNet, Am. J. Physiol. Heart Circ. Physiol., vol. 322, no. 4, 2021, doi: 10.1152/ajpheart.00392.2021.
[63]. F. Shoeibi, E. Najafiaghdam, and A. Ebrahimi, Poincaré’s section analysis of photoplethysmography signals for cuff-less non-invasive blood pressure measurement, Res. Square, 2021, doi: 10.21203/rs.3.rs-171469/v1.
[64]. Y. A. Djawad, A. Mu’nisa, P. Rusung, A. Kurniawan, I. S. Idris, and M. Taiyeb, Essential feature extraction of photoplethysmography signal of men and women in their 20s, Eng. J., vol. 21, no. 4, pp. 259–268, 2017, doi: 10.4186/ej.2017.21.4.259.
[65]. S. Iqbal, J. Bacardit, B. Griffiths, and J. Allen, Deep learning classification of systemic sclerosis from multi-site photoplethysmography signals, Front. Physiol., vol. 14, 2023, doi: 10.3389/fphys.2023.1242807.
[66]. F. S. Köklükaya and M. Öztürk, Blood pressure and heart rate estimation via TQWT-based decomposition of PPG signals, 2023.
[67]. R. Maccay and R. Weerasekera, Machine learning assisted postural movement recognition using photoplethysmography (PPG), arXiv, 2024, doi: 10.48550/arxiv.2411.11862.
[68]. X. Hu et al., Blood pressure stratification using photoplethysmography and light gradient boosting machine, Front. Physiol., vol. 14, 2023, doi: 10.3389/fphys.2023.1072273.
[69]. S. Abdullah and A. Kristoffersson, Machine learning approaches for cardiovascular hypertension stage estimation using photoplethysmography and clinical features, Front. Cardiovasc. Med., vol. 10, 2023, doi: 10.3389/fcvm.2023.1285066.
[70]. G. Frederick, T. Yaswant, and B. T. A., PPG signals for hypertension diagnosis: A novel method using deep learning models, arXiv, 2023, doi: 10.48550/arxiv.2304.06952.
[71]. Z. Papalamprakopoulou, D. N. Stavropoulos, S. Moustakidis, D. V. Avgerinos, M. Efremidis, and P. N. Kampaktsis, Artificial intelligence-enabled atrial fibrillation detection using smartwatches: Current status and future perspectives, Front. Cardiovasc. Med., vol. 11, 2024, doi: 10.3389/fcvm.2024.1432876.
[72]. D. Seok, S.-H. Lee, M. Kim, J. Cho, and C. Kim, Motion artifact removal techniques for wearable EEG and PPG sensor systems, Front. Electron., vol. 2, 2021, doi: 10.3389/felec.2021.685513.
[73]. C. Ding, R. Xiao, W. Wang, E. Holdsworth, and X. Hu, Photoplethysmography-based atrial fibrillation detection: An updated review from July 2019, arXiv, 2023, doi: 10.48550/arxiv.2310.14155.
[74]. P. Karimpour, J. M. May, and P. A. Kyriacou, Photoplethysmography for the assessment of arterial stiffness, Sensors, vol. 23, no. 24, p. 9882, 2023, doi: 10.3390/s23249882.
[75]. G. Nie et al., A review of deep learning methods for photoplethysmography data, arXiv, 2024, doi: 10.48550/arxiv.2401.12783.
[76]. N. Nasir, M. Sameer, F. Barneih, O. Alshaltone, and M. Ahmed, Deep learning classification of photoplethysmogram signal for hypertension levels, arXiv, 2024, doi: 10.48550/arxiv.2405.14556.
[77]. S. Kwon et al., Deep learning approaches to detect atrial fibrillation using photoplethysmographic signals: Algorithms development study, JMIR Mhealth Uhealth, vol. 7, no. 6, 2019, doi: 10.2196/12770.
[78]. E. Lan, HDformer: A higher-dimensional transformer for detecting diabetes utilizing long-range vascular signals, in Proc. AAAI Conf. Artif. Intell., vol. 38, no. 12, pp. 13320–13328, 2024, doi: 10.1609/aaai.v38i12.29233.
[79]. C.-C. Huang et al., Examining arterial pulsation to identify and risk-stratify heart failure subjects with deep neural network, Phys. Eng. Sci. Med., vol. 47, no. 2, pp. 477–490, 2024, doi: 10.1007/s13246-023-01378-6.
[80]. A. Qtaishat, W. Suryani, and W. S. W. Awang, Systematic review: Opportunities and challenges of machine learning techniques for cardiovascular disease prediction, J. Southwest Jiaotong Univ., vol. 59, no. 2, 2024, doi: 10.35741/issn.0258-2724.59.2.8.
[81]. X. Wen et al., Clinlabomics: Leveraging clinical laboratory data by data mining strategies, BMC Bioinformatics, vol. 23, no. 1, 2022, doi: 10.1186/s12859-022-04926-1.
[82]. H. H. Jung, H. Lee, J. Yea, and K. Jang, Wearable electrochemical sensors for real-time monitoring in diabetes mellitus and associated complications, Soft Sci., vol. 4, no. 2, 2024, doi: 10.20517/ss.2024.02.
[83]. T. Wang, W. Li, and D. M. Lewis, Blood glucose forecasting using LSTM variants under the context of open source artificial pancreas system, in Proc. Hawaii Int. Conf. Syst. Sci., 2020, doi: 10.24251/hicss.2020.397.
[84]. H. P. Tripathy, P. Pattanaik, D. K. Mishra, S. K. Kamilla, and W. Holderbaum, Experimental and probabilistic model validation of ultrasonic MEMS transceiver for blood glucose sensing, Sci. Rep., vol. 12, no. 1, 2022, doi: 10.1038/s41598-022-25717-x.
[85]. K. Liu et al., Machine learning models for blood glucose level prediction in patients with diabetes mellitus: Systematic review and network meta-analysis, JMIR Med. Inform., vol. 11, 2023, doi: 10.2196/47833.
[86]. A. Soliman, A. M. Nor, O. Fratu, S. Halunga, O. A. Omer, and A. S. Mubark, Non-invasive glucose level monitoring from PPG using a hybrid CNN-GRU deep learning network, arXiv, 2024, doi: 10.48550/arxiv.2411.11094.
[87]. S. Kriventsov, A. Lindsey, and A. Hayeri, The Diabits app for smartphone-assisted predictive monitoring of glycemia in patients with diabetes: Retrospective observational study, JMIR Diabetes, vol. 5, no. 3, 2020, doi: 10.2196/18660.
[88]. G. Frontino et al., Future perspectives in glucose monitoring sensors, US Endocrinol., vol. 9, no. 1, pp. 21–27, 2013, doi: 10.17925/use.2013.09.01.21.
[89]. A. Singhal and P. Singhal, Analysis of non-invasive devices and techniques involved in continuous monitoring of interstitial glucose of patients with type 1 diabetes, Int. J. Res. Appl. Sci. Eng. Technol., vol. 11, no. 12, pp. 1993–2002, 2023, doi: 10.22214/ijraset.2023.57784.