{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T13:31:33Z","timestamp":1777037493048,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UK Dementia Research Institute at Imperial College London"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 nm) is used for the acquisition of photoplethysmography (PPG). For rigor, we considered a full range of diabetic conditions (non-diabetic, pre-diabetic, type I diabetic, and type II diabetic). Recordings spanned nine different days, starting in the morning while fasting, up to a minimum of a two-hour period after eating a carbohydrate-rich breakfast. The BGLs from PPG were estimated using a suite of regression-based machine learning models, which were trained on characteristic features of PPG cycles pertaining to high and low BGLs. The analysis shows that, as desired, an average of 82% of the BGLs estimated from PPG lie in region A of the Clarke error grid (CEG) plot, with 100% of the estimated BGLs in the clinically acceptable CEG regions A and B. These results demonstrate the potential of the ear canal as a site for non-invasive blood glucose monitoring.<\/jats:p>","DOI":"10.3390\/s23063319","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T06:35:28Z","timestamp":1679466928000},"page":"3319","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3891-4783","authenticated-orcid":false,"given":"Ghena","family":"Hammour","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8432-3963","authenticated-orcid":false,"given":"Danilo P.","family":"Mandic","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14790","DOI":"10.1038\/s41598-020-71908-9","article-title":"Global, regional, and national burden and trend of diabetes in 195 countries and territories: An analysis from 1990 to 2025","volume":"10","author":"Lin","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.4103\/2468-8827.184853","article-title":"WHO Global report on diabetes: A summary","volume":"1","author":"Roglic","year":"2016","journal-title":"Int. J. Noncommun. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41746-021-00394-8","article-title":"Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data","volume":"4","author":"Ravaut","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"109119","DOI":"10.1016\/j.diabres.2021.109119","article-title":"IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045","volume":"183","author":"Sun","year":"2021","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.amjmed.2005.07.052","article-title":"Monitoring glycemic control: The importance of self-monitoring of blood glucose","volume":"118","author":"Renard","year":"2005","journal-title":"Am. J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.aca.2011.07.024","article-title":"Technology behind commercial devices for blood glucose monitoring in diabetes management: A review","volume":"703","author":"Vashist","year":"2011","journal-title":"Anal. Chim. Acta"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1177\/193229680800200526","article-title":"Finger pricking and pain: A never ending story","volume":"2","author":"Heinemann","year":"2008","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.diabres.2009.11.014","article-title":"Time lag characterization of two continuous glucose monitoring systems","volume":"87","author":"Garg","year":"2009","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1177\/19322968211007212","article-title":"Products for monitoring glucose levels in the human body with noninvasive optical, noninvasive fluid sampling, or minimally invasive technologies","volume":"16","author":"Shang","year":"2022","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"20190052","DOI":"10.1515\/bams-2019-0052","article-title":"Modern noninvasive methods for monitoring glucose levels in patients: A review","volume":"15","author":"Dziergowska","year":"2019","journal-title":"Bio-Algorithms Med-Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.bspc.2015.01.005","article-title":"Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy","volume":"18","author":"Yadav","year":"2015","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Islam, T.T., Ahmed, M.S., Hassanuzzaman, M., Bin Amir, S.A., and Rahman, T. (2021). Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning. Appl. Sci., 11.","DOI":"10.3390\/app11020618"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"13564","DOI":"10.1109\/JSEN.2021.3069460","article-title":"PPG-Based Smart Wearable Device With Energy-Efficient Computing for Mobile Health-Care Applications","volume":"21","author":"Lee","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Susana, E., Ramli, K., Murfi, H., and Apriantoro, N.H. (2022). Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal. Information, 13.","DOI":"10.3390\/info13020059"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1109\/TBCAS.2020.2979514","article-title":"A Noninvasive Glucose Monitoring SoC Based on Single Wavelength Photoplethysmography","volume":"14","author":"Hina","year":"2020","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hina, A., Nadeem, H., and Saadeh, W. (2019, January 26\u201329). A Single LED Photoplethysmography-Based Noninvasive Glucose Monitoring Prototype System. Proceedings of the 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo, Japan.","DOI":"10.1109\/ISCAS.2019.8702747"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.snb.2019.01.121","article-title":"Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring","volume":"286","author":"Rachim","year":"2019","journal-title":"Sens. Actuators B Chem."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.1115\/1.4036580","article-title":"Investigations on Multisensor-Based Noninvasive Blood Glucose Measurement System","volume":"11","author":"Yadav","year":"2017","journal-title":"J. Med. Devices"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1111\/j.1399-6576.2007.01375.x","article-title":"Combined photoplethysmographic monitoring of respiration rate and pulse: A comparison between different measurement sites in spontaneously breathing subjects","volume":"51","author":"Nilsson","year":"2007","journal-title":"Acta Anaesthesiol. Scand."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1109\/JSEN.2015.2471183","article-title":"In-Ear EEG From Viscoelastic Generic Earpieces: Robust and Unobtrusive 24\/7 Monitoring","volume":"16","author":"Goverdovsky","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6948","DOI":"10.1038\/s41598-017-06925-2","article-title":"Hearables: Multimodal physiological in-ear sensing","volume":"7","author":"Goverdovsky","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/TBME.2019.2911423","article-title":"Hearables: Automatic Overnight Sleep Monitoring with Standardized In-Ear EEG Sensor","volume":"67","author":"Nakamura","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MPUL.2012.2216717","article-title":"The In-the-Ear Recording Concept: User-Centered and Wearable Brain Monitoring","volume":"3","author":"Looney","year":"2012","journal-title":"IEEE Pulse"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hammour, G.M., and Mandic, D.P. (2021, January 1\u20135). Hearables: Making Sense from Motion Artefacts in Ear-EEG for Real-Life Human Activity Classification. Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Virtual.","DOI":"10.1109\/EMBC46164.2021.9629886"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hammour, G., Yarici, M., Rosenberg, W.V., and Mandic, D.P. (2019, January 23\u201327). Hearables: Feasibility and Validation of In-Ear Electrocardiogram. Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857547"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Davies, H.J., Williams, I., Peters, N.S., and Mandic, D.P. (2020). In-Ear SpO2: A Tool for Wearable, Unobtrusive Monitoring of Core Blood Oxygen Saturation. Sensors, 20.","DOI":"10.3390\/s20174879"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Davies, H.J., Williams, I., Hammour, G., Yarici, M., Seemungal, B.M., and Mandic, D.P. (2022). In-Ear SpO2 for Classification of Cognitive Workload. IEEE Trans. Cogn. Dev. Syst., accepted.","DOI":"10.1109\/TCDS.2022.3196841"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Davies, H.J., Bachtiger, P., Williams, I., Molyneaux, P.L., Peters, N.S., and Mandic, D. (2022). Wearable In-Ear PPG: Detailed Respiratory Variations Enable Classification of COPD. IEEE Trans. Biomed. Eng., in print.","DOI":"10.1109\/TBME.2022.3145688"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-319-47319-2_1","article-title":"FPGA Based Smart System for Non Invasive Blood Glucose Sensing Using Photoplethysmography and Online Correction of Motion Artifact","volume":"22","author":"Ramasahayam","year":"2017","journal-title":"Smart Sens. Meas. Instrum."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Perez, G.M., Mishra, K.K., Tiwari, S., and Trivedi, M.C. (2018). Networking Communication and Data Knowledge Engineering, Springer.","DOI":"10.1007\/978-981-10-4600-1"},{"key":"ref_31","first-page":"1736","article-title":"Noninvasive blood glucose monitoring system based on near-infrared method","volume":"10","author":"Mouhadjer","year":"2020","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_32","unstructured":"(2022). MATLAB Machine Learning and Deep Learning Toolbox, MATLAB R2022a, The MathWorks."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s12291-021-00971-4","article-title":"Clarke Error Grid Analysis for Performance Evaluation of Glucometers in a Tertiary Care Referral Hospital","volume":"37","author":"Sengupta","year":"2022","journal-title":"Indian J. Clin. Biochem."},{"key":"ref_34","unstructured":"(2015). In Vitro Diagnostic Test Systems. Requirements for Blood-Glucose Monitoring Systems for Self-Testing in Managing Diabetes Mellitus (Standard No. BS ENISO 15197:2015)."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Fine, J., Branan, K.L., Rodriguez, A.J., Boonya-Ananta, T., Ramella-Roman, J.C., McShane, M.J., and Cot\u00e9, G.L. (2021). Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring. Biosensors, 11.","DOI":"10.3390\/bios11040126"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3319\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:59:56Z","timestamp":1760122796000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3319"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,21]]},"references-count":35,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23063319"],"URL":"https:\/\/doi.org\/10.3390\/s23063319","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,21]]}}}