Non-Invasive Near-Infrared Blood Glucose Monitoring: Current Technologies, Patent Landscape, and Future Perspectives [version 1; peer review: awaiting peer review]

Дата публикации: 04-08-2026 07:43:19

Diabetes mellitus is a chronic metabolic disorder affecting more than 10 % of the global adult population, with projections estimating 783 million cases by 2045. Traditional methods for blood glucose monitoring including finger pricking and continuous glucose monitoring are clinically effective but remain invasive, uncomfortable and costly. In response, noninvasive alternatives have garnered growing interest driven by advances in biomedical technologies and the need for more patient friendly solutions. This article provides a comprehensive review of existing glucose monitoring techniques, examines the evolution of noninvasive devices and associated patents, and identifies the primary technical barriers to their widespread adoption. A comparative assessment highlights near infrared spectroscopy as a promising modality. We then explore how artificial intelligence can enhance calibration and prediction accuracy in near infrared spectroscopy (NIRS) based systems. Finally, the review discusses a prototype protected by an accepted patent as a technological perspective illustrating one possible direction for future non-invasive glucose monitoring systems.

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1. Introduction

Diabetes mellitus (DM) is a chronic, non-communicable metabolic disorder that has become one of the most pressing global public health challenges. Characterized by persistent hyperglycemia resulting from impaired insulin secretion, insulin action, or both, the disease is associated with profound metabolic disturbances involving carbohydrate, lipid, and protein metabolism. Over time, these alterations promote oxidative stress and contribute to the development of severe microvascular and macrovascular complications affecting the kidneys, eyes, peripheral nerves, and cardiovascular system.¹ According to the International Diabetes Federation (IDF) Diabetes Atlas (2021), diabetes affects 10.5% of adults aged 20–79 years worldwide, with nearly half of the affected individuals remaining undiagnosed. As illustrated in Fig. 1, based on data reported in.² the number of people living with diabetes continues to rise and is projected to reach approximately 783 million by 2045, representing an increase of 46% compared with current estimates. These projections underscore the urgent need for accurate, convenient, and patient-friendly glucose monitoring strategies capable of supporting effective long-term diabetes management.

Figure 1. People with diabetes aged 20–75 (2000–2045).

Glucose is the primary source of energy for cellular metabolism and is present not only in blood but also in other biological fluids, including interstitial fluid, saliva, tears, and urine.³ For individuals living with diabetes, regular glucose monitoring is essential for maintaining glycemic control through appropriate adjustments in medication, diet, and physical activity. Blood glucose concentration is commonly expressed in milligrams per deciliter (mg/dL), with the clinically relevant thresholds summarized in Table 1. Values below 70 mg/dL indicate hypoglycemia, whereas levels exceeding 180 mg/dL are generally associated with hyperglycemia, both of which may lead to serious acute and long-term complications if left uncontrolled.⁴^,⁵ Consequently, frequent glucose monitoring plays a central role in reducing the risk of glycemic excursions and improving overall diabetes management.⁶

Table 1. Different ranges of blood glucose levels.⁵State Range mg/dl Normal (fasting)70–130 hypoglycemiaBelow 70 hyperglycemiaAbove 180

Diabetes is broadly classified into three main categories: type 1 diabetes, type 2 diabetes, and gestational diabetes mellitus. Type 1 diabetes results from autoimmune destruction of pancreatic β-cells, leading to absolute insulin deficiency. Type 2 diabetes, which accounts for the majority of diabetes cases, is characterized by progressive β-cell dysfunction combined with insulin resistance. Gestational diabetes develops during pregnancy in individuals without previously diagnosed diabetes. In addition, less common forms include monogenic diabetes syndromes, diseases affecting the exocrine pancreas, and drug- or chemically induced diabetes.⁷

Current diabetes management relies primarily on invasive glucose measurement techniques. Conventional self-monitoring of blood glucose (SMBG) is based on electrochemical sensing and requires capillary blood obtained through repeated finger-pricking using disposable test strips. Although this method provides rapid and reliable glucose measurements without requiring specialized training, frequent skin puncture is often associated with pain, discomfort, reduced patient compliance, and an increased risk of local infection.⁸^,⁹ To overcome some of these limitations, continuous glucose monitoring (CGM) systems have been developed to provide real-time assessment of glucose fluctuations throughout the day. These minimally invasive devices improve glycemic control by facilitating timely therapeutic adjustments and optimizing insulin administration, thereby reducing the risk of diabetes-related complications. Nevertheless, CGM sensors still require subcutaneous insertion, and prolonged use may result in tissue irritation, inflammation, or damage at the insertion site, while their cost and limited sensor lifetime remain additional barriers to widespread adoption.¹⁰

These limitations have stimulated extensive research into non-invasive glucose monitoring technologies capable of providing accurate, painless, and continuous glucose assessment. Among the numerous sensing approaches investigated, optical techniques have attracted particular attention because of their potential for safe, real-time, and reagent-free glucose measurement. Despite considerable technological progress, however, most proposed systems still face important challenges related to measurement accuracy, physiological variability, calibration robustness, and long-term reliability, preventing their routine clinical implementation.¹¹ These persistent challenges raise an important question: what are the current technological advances, remaining challenges, and future opportunities in non-invasive blood glucose monitoring, particularly for optical and near-infrared (NIR)-based sensing approaches?

To address this question, this review provides a comprehensive synthesis of recent advances in non-invasive blood glucose monitoring by integrating scientific literature, bibliometric analysis, patent landscape assessment, and recent developments in artificial intelligence. In addition, the review discusses a prototype protected by an accepted patent as a proposed technological solution derived from the identified research gaps and future perspectives. By combining these complementary viewpoints within a single manuscript, this review aims to provide researchers with a comprehensive understanding of the current state of the field while highlighting promising directions for future research and technological development.

2. Overview of diabetes

Diabetes is a chronic metabolic disorder characterized by elevated blood sugar levels resulting from inadequate insulin production, insulin resistance, or both.¹² Type 1 diabetes mellitus (T1D) is an autoimmune condition that results in the destruction of insulin-producing beta cells in the pancreas. People with T1D need lifelong insulin therapy, which can be administered through multiple daily injections, insulin pump therapy, or an automated insulin delivery system.¹³

This autoimmune condition affects millions of people globally and requires careful management to avoid serious long-term complications, including heart and kidney disease, stroke, and loss of sight.¹⁴

Type 2 Diabetes Mellitus (T2DM) is a prevalent metabolic disorder primarily driven by two key issues: impaired insulin secretion from pancreatic β-cells and the inability of insulin-sensitive tissues to respond effectively to insulin. Insulin release and function are essential for maintaining glucose balance in the body, and the processes governing insulin production, release, and action are highly regulated. Disruptions in any of these processes can lead to metabolic imbalances that contribute to the onset of T2DM.¹⁵

As one of the most significant global health challenges, individuals with T2D face a cardiovascular mortality risk that is twice as high as that of the general population. Among the many complications associated with T2D, heart failure (HF) deserves special attention, as it is one of the leading causes of morbidity and reduced life expectancy.¹⁶

Type 1 diabetes and type 2 diabetes are heterogeneous diseases whose genetic and environmental factors can cause a progressive loss of β cell mass and/or function, leading to clinical hyperglycemia. Although both types of diabetes share this fundamental mechanism, their clinical manifestations and course can vary considerably. Once hyperglycemia sets in, patients with all forms of diabetes are at risk of developing similar chronic complications, although the rate of progression may differ. Accurate classification of these types of diabetes is essential to determine the appropriate treatment. However, some individuals cannot be clearly classified as type 1 or type 2 diabetics at the time of diagnosis, which can complicate the choice of treatment.¹⁷^,¹⁸

To round out the discussion on diabetes types, it is essential to consider gestational diabetes, which occurs during pregnancy and can significantly impact the baby’s health. Pregnancy often leads to increased insulin resistance and decreased insulin sensitivity. Elevated estrogen levels towards the end of the first trimester can improve insulin sensitivity but may also increase the risk of hypoglycemia in the mother due to associated nausea and vomiting. The rise in insulin resistance during pregnancy is primarily driven by a combination of placental hormones, including human placental lactogen, progesterone, prolactin, placental growth hormone, and cortisol. More recently, tumor necrosis factor α and leptin have been identified as contributing to this state of insulin resistance during pregnancy.¹⁹

High blood glucose levels during pregnancy can lead to various problems for the baby, such as premature birth, high birth weight, which complicates delivery and can cause injury, as well as immediate postnatal hypoglycemia and breathing issues. These complications highlight the importance of effective management of gestational diabetes to minimize risks to the baby. Additionally, gestational diabetes increases the likelihood of developing type 2 diabetes later in life. Prolonged high glucose levels can also result in complications such as diabetic retinopathy, heart disease, kidney disease, and nerve damage. Nevertheless, there are strategies that can help prevent or delay the onset of type 2 diabetes.²⁰ The most common types of diabetes include type 1, type 2, and gestational diabetes, with less common forms such as monogenic and ketotic diabetes also existing.²¹

3. Skin structure and properties

The skin consists of seven layers grouped into three primary tissue types: the epidermis, dermis, and hypodermis, each characterized by distinct optical and electromagnetic properties that influence the penetration depth and interaction with specific wavelengths and frequencies.²²^,²³ These interactions can be leveraged to assess tissue structure and composition, including the concentration of various constituents such as glucose, water, proteins, lipids, and electrolytes.

4. Literature on glucose monitoring technologies

4.1 Traditional techniques

Self-monitoring blood glucose (SMBG)

Since its introduction in the 1970s, the concept of routine self-monitoring of blood glucose has become a fundamental aspect of diabetes management. It transformed the field by providing affordable and easy-to-use devices, allowing individuals to check their glucose levels independently at home, without needing to visit a healthcare provider. Among these methods, the traditional finger-prick technique using glucose test strips (as shown in Fig. 2) remains the most reliable option for personal use, apart from laboratory-based reference techniques. The fingertip is typically chosen because it is easily accessible, well-supplied with blood, and convenient for applying a small drop onto the strip. This method relies on two main components: a strip coated with enzymes that react specifically with glucose, such as glucose oxidase (GOx), glucose dehydrogenase (GDH) or hexokinase (HK), and a digital reader. When blood is placed on the strip, the glucose reacts with the enzyme, generating a signal that is converted by the reader into a current directly reflecting the glucose concentration in the blood.⁶ In certain clinical situations, venous blood may be used in place of a capillary sample. However, since most point-of-care glucometers are specifically designed for capillary blood, using venous blood could result in inaccurate or misleading readings.²⁴

Figure 2. Conventional blood glucose monitoring device.

Glucose detection at electrodes can be achieved through three main methods. The first involves measuring oxygen consumption, which decreases as glucose is oxidized. The second method measures the concentration of hydrogen peroxide (H₂O₂) produced during glucose oxidation at the electrode. These two techniques represent the first generation of glucose biosensors. The third method, considered the second generation, uses a chemical mediator to transfer electrons to the electrode interface for glucose detection. Advancing to the third generation, biosensors eliminate the need for toxic mediators by enabling direct electron transfer to the electrode surface, offering a safer and more efficient detection process.²⁵

Finger-pricking for glucose monitoring presents several challenges. Many individuals find the use of sharp objects and exposure to blood uncomfortable, and repeated pricking increases the risk of infection and long-term damage to finger tissue. As blood sampling becomes more frequent, wounds often struggle to heal, raising the chance of external infections, while the pain and stress associated with daily pricking make it a burdensome process. Additionally, this method only provides discrete measurements, leaving episodes of hyperglycemia or hypoglycemia undetected between tests, which limits the accuracy of glucose monitoring. The cost of disposable test strips and lancets, coupled with their limited shelf life and the potential for improper storage affecting measurement accuracy, adds further strain, particularly for families in underdeveloped regions.²⁶^,²⁷

4.2 Evolution towards Continuous Glucose Monitoring (CGM)

a. Continuous glucose monitoring (CGM)

Following the SMBG era, which spanned the 1980s to 2000s, diabetes management underwent a paradigm shift with the advent of continuous glucose monitoring (CGM). Over the past two decades, CGM has demonstrated its transformative potential, revolutionizing diabetes care through its ability to provide precise and continuous glucose monitoring.²⁸

Continuous Glucose Monitoring (CGM) (Fig. 3) are minimally invasive devices that emerged as a cornerstone of smart healthcare, offering advanced tools for managing diabetes through near-continuous glucose data. Unlike traditional meters, CGM systems estimate blood glucose levels from interstitial fluid using sophisticated algorithms, enabling real-time monitoring and informed decision-making in daily diabetes care. These systems feature customizable alerts for hypo- and hyperglycemia and provide trends on glucose level changes, offering a comprehensive view of glucose fluctuations.²⁹

Figure 3. a) minimally invasive device for continuous glucose monitoring.

b) Needle of a CGM device after being removed from an arm.

Comprising a wireless receiver, transmitter, and sensor, CGM devices are seamlessly integrated into smart health ecosystems. The receiver displays glucose readings, while the transmitter, attached to the sensor, sends data via radio frequency (RF) waves.³⁰ The most widely used technique for continuous glucose monitoring (CGM) systems is based on the glucose oxidation reaction. CGM devices based on this principle utilize a platinum electrode coated with glucose oxidase, which is inserted into the subcutaneous tissue via a needle. This setup catalyzes the oxidation of glucose, generating gluconolactone, hydrogen peroxide, and an electrical current. The resulting electrical signal is then converted into glucose concentration through a calibration process using a limited number of self-monitoring blood glucose (SMBG) samples provided by the patient.³¹^,³² This integration of CGM technology into smart health systems enhances personalized care, offering patients continuous and proactive management of their condition.

Compared with the traditional single point blood glucose detection, CGM offers both retrospective and real-time data, enabling the detection of hypoglycemic and hyperglycemic events, predicting impending hypoglycemia, and identifying significant glucose level fluctuations, known as glycemic variability. All FDA-approved CGM devices come with 24-hour telephone support. Utilizing CGM helps patients and healthcare providers fine-tune medication adjustments and offers valuable insights into behavioral changes necessary for achieving glycemic control. Additionally, ongoing advancements in linking CGM with automated insulin delivery systems are gradually moving closer to the realization of a fully functional artificial pancreas. CGM systems are classified into professional CGM (clinic-owned, providing either retrospective or real-time data) and personal CGM (patient-owned, offering real-time data).³³

Directly connecting blood glucose or CGM data to smartphones or other devices enhances data accuracy and facilitates the integration of glucose levels with other health data, such as insulin usage, carbohydrate intake, and physical activity. This connectivity supports real-time or retrospective insulin dose adjustments and may improve communication with healthcare providers. Some devices with cellular or Bluetooth capabilities can pair with apps to help collect, communicate, and analyze data, providing tools for patient education, such as nutrition information, directly at the point of care.³³

Continuous glucose monitoring systems (CGM), despite their many advantages, have limitations. First, they introduce an inevitable delay between the measurement of blood glucose and that in the interstitial fluid, which can reach 15 to 20 minutes. In addition, the lifespan of biosensors is limited, requiring frequent replacements. Calibration by fingerstick is often necessary to ensure the accuracy of measurements. Finally, CGMs may have difficulty measuring glucose levels outside of a certain range.³⁴ These challenges have spurred research efforts to develop less invasive or non-invasive CGM systems.

4.3 Non-invasive glucose monitoring

To clarify our scope, we define non-invasive techniques as those that do not require any skin penetration, offering more comfort and continuous measurement potential compared to invasive or semi-invasive approaches.³⁵^,³⁶ Optical-based methods (Fig. 4) are particularly prominent in recent studies due to their real-time capability and promising accuracy.³⁷^,³⁸

Figure 4. Overview of non-invasive blood glucose monitoring techniques.

4.3.1 Optical monitoring

To better understand the operational principles, advantages, and limitations of the main optical techniques explored for non-invasive glucose monitoring, a comparative summary is presented in Table 2. These approaches represent the most commonly studied modalities in the literature. In addition to conventional blood-based targets, these techniques aim to detect glucose levels using alternative biofluids or tissues such as saliva, sweat, skin, ocular fluids, fingertip, or ear cartilage, instead of blood.

Table 2. Comparative overview of main optical techniques for non-invasive glucose monitoring.³⁹TechniqueOperating PrincipleAdvantagesLimitations NIR SpectroscopyAbsorption of light in the 780–2500 nm range, targeting skin areas with thin layers and rich vascularization.Well-studied, suitable for live tissues, compatible with portable devices.Results affected by skin condition and hydration, variability in parameters, lack of standardization.MIR SpectroscopyDetects fundamental vibrational absorptions in the 2.5–25 μm range (4000–400 cm⁻¹), glucose peaks at 8–10 μm.Higher selectivity, sharper absorption bands, less scattering than NIR.Limited skin penetration, high dependence on tissue water content.Raman SpectroscopyMeasures inelastic scattering of monochromatic light to identify molecular vibrations (C–H, C–O, C–C).High molecular specificity, contactless measurement possible.Weak Raman signal, long acquisition times, biocompatibility and substrate toxicity issues.Photoacoustic SpectroscopyDetects ultrasonic waves produced by short laser pulses absorbed by tissue and converted into heat.Selective detection via QCL or FTIR, direct glucose measurement from acoustic signal.Expensive MIR sources (QCL), weaker signal in NIR, sensitivity to motion.Fluorescence SensingExcites molecules with UV or visible light, measures emitted fluorescence at lower energy.High sensitivity, low-cost light sources, promising for serum and subcutaneous detection.Low selectivity, strong signal interference, sensitive to skin scattering properties.

In addition to the main optical and electromagnetic approaches, various alternative noninvasive techniques have been investigated. Thermal emission spectroscopy (TES) measures the natural infrared radiation emitted by the skin, which varies with glucose concentration, while microwave spectroscopy (MWS) analyzes changes in dielectric properties induced by glucose using microwave signals. Metabolic heat conformation (MHC) estimates glucose levels through physiological parameters linked to heat production in the body. Photoacoustic spectroscopy (PAS) detects acoustic waves generated by laser induced thermal expansion, whereas occlusion spectroscopy assesses changes in light scattering after temporary blood flow restriction. Other methods such as optical polarimetry, optical coherence tomography (OCT), bioimpedance spectroscopy, and electromagnetic sensing rely on tissue optical or electrical responses to glucose fluctuations. Finally, noninvasive enzymatic techniques explore alternative body fluids such as saliva, sweat, and tears for glucose detection, although their correlation with blood glucose remains limited.⁴⁰

To assess which of these techniques are most actively investigated, we performed a bibliometric analysis based on the following structured query:

(“non-invasive” OR “noninvasive”) AND (“glucose monitoring” OR “blood glucose” OR “glycemia”) AND (spectroscopy OR “near infrared” OR “NIR” OR “Raman” OR “photoacoustic” OR “fluorescence” OR “microwave” OR “bioimpedance” OR “electromagnetic” OR “mid infrared” OR “thermal” OR “optical coherence tomography” OR “polarimetry”)

The retrieved bibliographic records were analyzed using VOSviewer (version 1.6.20) to generate a keyword co-occurrence network (Fig. 5), revealing the relationships among the most frequently occurring keywords in the field.

Figure 5. Keyword Co-occurrence network in non-invasive blood glucose monitoring research.

This co-occurrence map reveals that terms such as “near-infrared spectroscopy”, “infrared”, and “photoacoustic” appear prominently and centrally within the research landscape, indicating their strong association with non-invasive glucose monitoring. These techniques not only occur frequently but also show dense interconnections with other relevant concepts, suggesting that they have become central to ongoing investigations in the field. In particular, the prominence of near-infrared spectroscopy (NIRS) highlights its perceived potential and growing relevance among researchers. This observation provides a compelling rationale for the methodological orientation of the present work, which focuses on the application of NIRS as a non-invasive technique for glucose measurement.

5. Patent analysis of non-invasive blood glucose monitoring technologies

Patent CN102018517A, titled “Non-Invasive Glucometer,” was invented by Lin Ziyi and filed in China under application number CN2009100929676A. The patent was filed on September 17, 2009, with the same priority date, and was published on April 20, 2011.

The patent describes a wrist-worn, non-invasive blood glucose meter designed to measure blood glucose concentration without requiring blood sampling. Unlike traditional invasive glucometers that rely on finger-pricking and test strips, this device utilizes near-infrared (NIR) spectroscopy or laser technology to estimate glucose levels by analyzing light absorption and reflection. The device is designed in the form of a bracelet that is secured around the wrist, ensuring stable and repeatable measurements. It consists of a protective housing that encloses the internal components, including a circuit board containing a control unit responsible for processing glucose data and a memory element for storing calibration curves and previous readings. The light emission module generates an infrared or laser beam, which penetrates the skin and interacts with glucose molecules in the bloodstream. The optical fiber receiving module collects the reflected or scattered light, and the control unit analyzes this signal by comparing it to pre-stored calibration curves to determine glucose concentration. The bracelet features an integrated display, allowing users to view glucose readings in real time, while a power supply unit ensures continuous operation, likely through a rechargeable battery.

The device functions by emitting infrared or laser light through the skin, where glucose molecules absorb specific wavelengths. The modified light signal is reflected back and captured by the optical fiber sensor, which transmits it to the control unit for analysis. The computed glucose concentration is then displayed on the bracelet’s screen, providing immediate feedback to the user. The patent also describes an alternative version featuring a detachable detecting pen, which is connected to the bracelet via an electrical lead. This detecting pen allows for localized glucose measurements beyond the wrist, enhancing the device’s versatility and usability.

This patented technology presents several advantages. By being completely non-invasive, it eliminates the need for finger-pricking and blood sampling, thereby reducing pain and the risk of infection. Its wearable and portable design enables continuous, real-time glucose tracking, and the integrated display allows for convenient instant monitoring. Moreover, the device does not require disposable test strips or lancets, making it a cost-efficient alternative to conventional blood glucose meters. However, there are certain limitations to this approach. The accuracy of optical measurements can be affected by skin properties, hydration levels, and external interference, making proper calibration essential. The device depends on pre-stored calibration curves, which may require updates to maintain measurement reliability. Additionally, further clinical validation is necessary to assess its effectiveness in comparison with standard invasive glucometers.

Patent CN213787419U, titled “Full-Automatic Painless Blood Glucose Meter”, introduces an innovative device designed to overcome the limitations of traditional glucose monitoring methods, which typically involve finger-pricking and manual blood collection. These conventional techniques often cause pain, discomfort, and anxiety for patients, and they usually require assistance from medical staff.

The patented system eliminates these drawbacks by using a laser-based blood sampling mechanism. This approach employs a focused infrared laser beam to heat dermal capillaries, causing localized vasodilation and pressure buildup that brings blood to the surface without physical penetration. This non-contact, painless extraction method significantly reduces infection risks and enhances patient comfort, particularly for individuals with needle phobia or the elderly.

The device architecture integrates multiple automated modules:

• A laser sampling assembly combined with a rotating turntable for positioning and replacing sampling caps.
• A fully automated test strip detection and disposal system to manage strips efficiently and maintain hygiene.
• A glucose detection PCB board that analyzes blood samples and transmits results in real time.

The system also features a touch-sensitive display with LED indicators to guide users step by step. A camera-based identification module allows facial recognition or ID-based authentication, making it suitable for hospital use. Furthermore, the device supports cloud connectivity through hospital health information systems (HISS) or mobile apps, enabling continuous remote monitoring by healthcare providers.

Key benefits include painless blood collection, minimized cross-contamination, automation of critical processes, and real-time data sharing. However, the technology presents challenges such as high production costs due to integrated laser and automation components, and potential variability in accuracy linked to skin characteristics, requiring further clinical validation.

Patent CN110191673A, titled “Noninvasive Blood Glucose Sensor”, was filed by Brazilian Lelif Technology Co. Ltd. and Basil Leaf Technologies LLC on November 3, 2017, and was published on August 30, 2019. Although the application was later withdrawn, it presents a noteworthy approach to noninvasive blood glucose monitoring through multiwavelength optical analysis.

The proposed invention consists of a sensor body designed to interface with the surface of the skin, integrating multiple light sources (blue, green, red, and infrared) and photodetectors. These components are mounted on a support structure that ensures proper anatomical alignment and spatial consistency, facilitating stable measurements at selected body sites such as the fingertip, wrist, or earlobe.

The core mechanism relies on the differential absorption and scattering of light at specific wavelengths, which vary according to the concentration of glucose in the blood. By emitting light from these sources and detecting the resulting optical signals, whether transmitted, reflected, or transreflected, the system captures tissue responses correlated with glucose content. A central controller receives these signals, digitizes them, and applies analytical models including calibration equations and regression-based algorithms to estimate the user’s blood glucose level.

One of the key contributions of this invention is the use of blue and green light wavelengths, which were empirically shown to be more sensitive to glucose-induced optical changes than the red and infrared bands typically used in pulse oximetry. The system architecture also supports wireless data transmission, allowing seamless integration with smartphones, health monitoring platforms, or clinical databases. Additionally, the modular design makes it adaptable to wearable formats such as smartwatches, finger clips, or patches.

Despite its advantages, which include noninvasiveness, ergonomic adaptability, and potential for real-time user-friendly monitoring, the system’s accuracy remains dependent on proper calibration and may be affected by individual variability in skin properties, motion artifacts, or ambient light conditions. Nonetheless, this patent illustrates a technically mature and well-integrated solution that advances the feasibility of optical noninvasive glucose sensing in wearable medical technologies.

Patent US20250000463A1, titled “Device and System User Interfaces for Chronic Health Condition Management,” was filed by Tula Health, Inc. (Kaysville, UT, USA), with inventors David Miller, Devin Miller, Michael Jones, and David Derrick on July 1, 2024, and later published on January 2, 2025.

The invention describes a health device network that integrates non-invasive and invasive glucose monitoring technologies with cloud-based data processing and interactive user interfaces. The system consists of a non-invasive glucometer that measures glucose levels without blood sampling, an invasive glucometer for traditional blood-based testing, a cloud-based server for aggregating and processing data, and a user device such as a smartphone or wearable that provides real-time glucose insights through a digital interface. The non-invasive glucometer employs optical or biosensor-based detection methods, enabling continuous monitoring, while periodic invasive measurements are used to improve calibration and accuracy. The data analytics application within the cloud-based server integrates both glucose measurement types, creating a comprehensive dataset for tracking trends, predicting fluctuations, and alerting users or healthcare providers to potential concerns.

The main advantages of this invention include its hybrid approach, combining non-invasive and invasive monitoring to ensure greater accuracy and reliability, along with real-time data processing, which synchronizes glucose readings with a cloud-based system for continuous tracking and long-term analysis. Additionally, the user-friendly interface enhances patient engagement by providing interactive glucose monitoring, predictive insights, and personalized alerts accessible through mobile devices and wearables. The system also allows remote monitoring, enabling healthcare providers to access patient data remotely, thereby improving disease management and clinical decision-making. However, certain limitations exist, including the system’s dependence on internet connectivity, which may affect usability in areas with poor network access. Additionally, frequent calibration between non-invasive and invasive readings is required to maintain accuracy, and the integration of advanced sensors, cloud computing, and AI-driven data processing may result in higher production costs, potentially limiting accessibility in low-resource settings.

A comparison of representative patents related to blood glucose monitoring technologies is presented in Table 3, highlighting differences in monitoring approach, sensing technology, device form factor, data processing, and output interface.

Table 3. Comparison of representative patents for blood glucose monitoring technologies.Patent NumberType of monitoringTechnology usedForm factorData processingDisplay & output CN102018517ANon-invasive NIR/laser spectroscopy with optical fiberWrist-worn braceletOn-device analysis with control unitIntegrated display CN213787419UMinimally invasive (laser blood extraction)Infrared laser sampling with automated strip handlingSelf-service kioskOn-device with real-time display and cloud syncDisplay module with web sync CN110191673ANon-invasive Multi-wavelength optical spectroscopy (blue, green, red, IR)Wearable/flexible sensor (e.g. clip, patch)Signal digitization + regression analysisWireless display integration US20250000463A1Hybrid (non-invasive + invasive)Optical/biosensor-based monitoring with cloud integrationWearable/smartphone connected systemCloud-based data integration and analysisSmartphone/wearable interface

6. Overview of non-invasive glucose monitoring devices and technologies

Non-invasive glucose measurement methods refer to technologies capable of estimating blood glucose levels without the need for skin penetration or blood extraction. These approaches represent a promising alternative to conventional methods, particularly for patients requiring frequent monitoring. Various devices have been developed based on these principles. For example, SugarBEAT (Nemaura Medical, UK) uses a disposable skin patch coupled with a transmitter, while GlucoWise (MediWise Ltd., UK) estimates glucose concentration by transmitting low-power radio waves through the earlobe. An overview of selected non-invasive glucose monitoring devices, together with their underlying technologies and target measurement sites, is presented in Table 4.

Table 4. Overview of selected non-invasive glucose monitoring devices, technologies, and target sites.Device nameTechnology usedMeasurement site Cnoga Medical Combo GlucometerNear-Infrared (NIR) SpectroscopyFingertip GlucoTrack® by Integrity ApplicationsCombination of thermal, ultrasonic, and electromagnetic signalsEarlobe tissue GlucoWise™ by MediWiseRadio wave spectroscopySkin between thumb and forefinger, earlobe GlucoWatch® G2 Biographer by Cygnus Inc.Reverse IontophoresisWrist skin SugarBEAT™ by Nemaura MedicalReverse IontophoresisArm, leg, or abdomen

7. Technical challenges

a. Signal attenuation due to high water content in biological tissues
Biological tissues contain a high amount of water, which strongly absorbs light, leading to significant signal attenuation. This makes it difficult to detect subtle spectral variations, especially those linked to glucose. The presence of various overlapping molecular signatures in tissue further complicates signal extraction and requires sophisticated processing and calibration to isolate glucose-related information.⁴¹^,⁴²
b. Low glucose concentration in body fluids
The glucose content in body fluids is relatively low (only 1% to 10% of the glucose density found in blood), making it challenging to detect small variations in glucose concentration, thus limiting the sensitivity of non-invasive methods.⁴¹
c. Fluctuations due to water evaporation and seasonal changes
The evaporation of water and seasonal variations in fluid volume can reduce the accuracy of glucose measurements in body fluids. This limitation prevents continuous, long-term, or sleep-based monitoring.⁴¹
d. Need for a high signal-to-noise ratio
Accurately detecting glucose values requires instruments capable of achieving a high signal-to-noise ratio. This is currently a significant challenge in non-invasive blood glucose detection technology, as achieving such a ratio is not yet feasible with existing devices.⁴¹
e. Measurement Precision
One of the key obstacles to implementing non-invasive glucose monitoring in clinical practice is ensuring the necessary level of accuracy and reliability. These technologies often face challenges from various sources of interference, such as other biomolecules, fluctuations in sensor performance, and environmental conditions like temperature and humidity, all of which can distort results.⁴³
f. Instability of Measurement Conditions
Variations in measurement conditions such as temperature or light angle, make it challenging to obtain stable and reliable glucose readings. Accurate detection requires a stable environment during the measurement process.⁴¹
g. Regulatory and Commercial Challenges
Non-invasive glucose monitoring technologies also encounter substantial regulatory hurdles. Health authorities impose strict performance and accuracy standards that must be met before approval. Furthermore, achieving market penetration remains difficult, as both healthcare providers and patients may be hesitant to adopt new devices until they demonstrate comparable clinical performance to traditional invasive methods.⁴³

8. Integration of artificial intelligence in NIR-Based glucose monitoring systems

Models based on artificial intelligence are able to learn device specific variations and compensate for sensor drift, reducing the reliance on manual calibration and preserving measurement accuracy over time. Since noninvasive methods do not directly measure blood glucose levels from blood samples, AI is increasingly being explored to estimate and predict glucose levels based on surrogate physiological features. While AI algorithms have found widespread use in various healthcare applications such as clinical decision support and hypoglycaemia alerts in type 1 diabetes T1DM, their integration with noninvasive monitoring technologies remains limited but promising.⁴⁴

Joshi et al.⁴⁵ proposed a wearable non-invasive glucose monitoring device based on short-wavelength near-infrared (NIR) light (940 nm and 1300 nm) in both absorbance and reflectance modes. Integrated with the Internet of Medical Things (IoMT), the system leverages a deep neural network (DNN) regression model to predict glucose levels using data collected from healthy, prediabetic, and diabetic participants aged 17 to 80. The model achieved higher accuracy in serum glucose prediction (AvgE: 4.88%; mARD: 4.86%) compared to capillary readings, with 100% of serum-based predictions falling within Clarke Error Grid zone A.

Similarly Dai et al.⁴⁶ developed a model using NIR absorbance at 1550 nm, employing a dual artificial neural network (ANN) structure optimized by Particle Swarm Optimization (PSO). This approach captures the nonlinear relationship between absorbance and glucose concentration. Clarke Error Grid analysis showed that 64% of predicted values were in region A and 29% in region B. However, the study’s limitation lies in its small dataset (only six healthy subjects) which underscores the need for broader validation on diabetic populations.

9. Patent-Based prototype: A technological perspective

9.1 Introduction to the proposed system

Conventional blood glucose monitoring continues to rely primarily on invasive and minimally invasive techniques, including finger-prick testing and subcutaneous continuous glucose monitoring systems. Although these approaches provide clinically reliable glucose measurements, they remain associated with several limitations, including patient discomfort, infection risk, frequent calibration requirements, limited sensor lifetime, and reduced long-term adherence. As discussed throughout this review, these limitations continue to motivate the development of alternative non-invasive technologies capable of providing accurate, continuous, and user-friendly glucose monitoring.

In light of the scientific advances and technological challenges identified in the literature, this section discusses a prototype protected by an accepted patent as a technological perspective for non-invasive blood glucose monitoring. Rather than presenting a clinically validated device, the prototype illustrates one possible system architecture inspired by the current state of the art and by the limitations highlighted throughout this review.

The patented concept combines near-infrared (NIR) spectroscopy for non-invasive glucose sensing with intelligent data processing and Internet of Things (IoT) connectivity to support real-time data transmission and remote patient monitoring. By integrating these complementary technologies within a single framework, the proposed architecture aims to illustrate a potential direction for the development of future non-invasive glucose monitoring systems that are more comfortable, accessible, and suitable for long-term diabetes management.

9.2 Working principle

The patent-based prototype operates according to the principles of near-infrared (NIR) spectroscopy, an optical sensing technique that exploits the interaction of near-infrared light with biological tissues to extract biochemical information. When the measurement site is illuminated, the incident radiation is partially absorbed, scattered, reflected, and transmitted by tissue constituents. Because glucose exhibits characteristic absorption features within specific regions of the NIR spectrum, the detected optical signal contains information related to glucose concentration, which can be further processed for glucose estimation.⁴⁷^,⁴⁸

NIR spectroscopy has attracted considerable attention for non-invasive glucose monitoring because of its potential to provide painless, real-time measurements. However, its practical implementation remains challenging owing to physiological variability, tissue heterogeneity, and interference from other biological constituents, all of which may affect measurement accuracy.⁴⁹^,⁵⁰ The patent-based prototype discussed in this review builds upon these established principles, while its overall architecture and functional components are described in the following section.

9.3 System architecture and functional blocks

The proposed non-invasive glucose monitoring system is structured around four main functional layers, as illustrated in Fig. 6: the sensing unit, the data transmission layer, the expert system, and the user interface.

Figure 6. System architecture for non-invasive glucose monitoring using NIR spectroscopy.

This modular architecture enables the integration of the main functionalities required for a modern non-invasive monitoring system, including NIR-based optical sensing, wireless communication, intelligent data processing, and real-time user feedback.

Such layered design not only facilitates system scalability and debugging but also allows independent optimization of each module, thereby accelerating the development cycle toward future clinical translation.

Its operation begins with a near-infrared (NIR) sensor, which emits light into the skin and analyzes the reflected or absorbed signals to extract information related to glucose concentration. Since no blood sample is required at any stage, the system is fully non-invasive, eliminating pain, discomfort, and the risk of infection commonly associated with finger-prick or implantable methods.

The collected optical signals are then processed by a microcontroller responsible for initial data acquisition and communication management. Data transmission can occur via two pathways:

• Via Wi-Fi: the data are sent to a cloud platform through an API, enabling centralized storage and remote access by healthcare providers.
• Via Bluetooth: the data are transmitted directly to a mobile application installed on the user’s smartphone.

Within the mobile application, an intelligent decision-support module is incorporated. This module integrates a knowledge base and an inference engine to interpret spectroscopic data and estimate glucose levels. This intelligent module comprises a knowledge base and an inference engine, which interprets spectroscopic data and estimates glucose levels through expert-level reasoning. The system also considers user-specific physiological parameters, enhancing the personalization of recommendations.

In addition to providing glucose level estimations, the expert system can generate real-time alerts and tailored advice to support users in managing their condition more effectively. All hardware components are powered by a rechargeable battery, making the device portable and suitable for continuous daily use.

Overall, the proposed architecture illustrates how optical sensing, intelligent data processing, and connected healthcare technologies may be integrated within a unified framework for future non-invasive glucose monitoring systems.

9.4 Design considerations for future wearable implementation

The successful implementation of non-invasive glucose monitoring systems depends not only on sensing accuracy but also on practical design considerations that promote long-term usability and patient acceptance. As highlighted throughout this review, future wearable devices should combine miniaturization, energy efficiency, user comfort, and seamless wireless connectivity within a compact and user-friendly platform.

Low-power operation is an important design objective for wearable glucose monitoring systems, as it contributes to prolonged device autonomy while supporting continuous data acquisition and communication. Likewise, ergonomic design is essential to ensure comfort during prolonged use and to encourage patient adherence. Depending on the intended application, wearable formats such as wristbands or skin patches may provide practical solutions for integrating non-invasive glucose monitoring into daily life.

The patent-based prototype discussed in this review was conceived with these design considerations in mind. As illustrated in Fig. 7, the envisioned wearable configuration represents the long-term objective of the patented concept, while the current functional prototype serves as an intermediate proof of concept supporting future engineering development and clinical translation.

Figure 7. Envisioned wearable design and current functional prototype of the patent-based non-invasive glucose monitoring system.

10. Discussion

The growing body of research reviewed in this article demonstrates the sustained interest in developing reliable non-invasive blood glucose monitoring technologies. Although numerous sensing strategies have been investigated, optical techniques have emerged as the most extensively explored owing to their potential for painless, continuous, and wearable glucose monitoring. Despite these advances, none of the currently available non-invasive approaches has yet achieved the level of accuracy and robustness required for routine clinical use. This limitation reflects not only the complexity of glucose detection itself but also the influence of physiological factors such as tissue heterogeneity, skin pigmentation, hydration status, blood perfusion, and inter-individual variability.

Among the optical methods discussed, near-infrared spectroscopy remains one of the most promising candidates because of its favorable characteristics for non-invasive sensing and its compatibility with portable and wearable devices. Nevertheless, the literature consistently indicates that improvements in sensing hardware alone are insufficient to overcome the remaining challenges. Increasing attention is therefore being directed toward artificial intelligence and advanced data analysis techniques capable of improving calibration, compensating for physiological variability, and enhancing prediction accuracy.

The integration of bibliometric and patent analyses further emphasizes the dynamic evolution of this field, revealing both sustained scientific activity and continuous technological innovation. At the same time, the limited number of clinically adopted devices highlights the gap that still exists between laboratory developments and practical healthcare applications. Within this context, the patent-based prototype discussed in this review is presented as a technological perspective illustrating how optical sensing, intelligent data processing, and connected healthcare technologies may be combined within a unified framework for future non-invasive glucose monitoring systems.

11. Future directions

The future of non-invasive blood glucose monitoring is expected to rely on the convergence of multiple complementary technologies rather than on improvements in individual sensing techniques alone. The combination of optical spectroscopy, artificial intelligence, personalized calibration strategies, and multimodal sensing is expected to improve measurement reliability while reducing the impact of physiological variability.

Equally important is the need for large-scale clinical validation involving diverse populations under real-world conditions. Standardized evaluation protocols and clinically relevant benchmarking methods will be essential for objectively comparing emerging technologies and facilitating their translation into clinical practice.

Continued progress in wearable electronics, low-power embedded systems, and connected healthcare platforms is also expected to accelerate the development of practical non-invasive monitoring devices. Within this evolving landscape, the patent-based prototype discussed in this review illustrates one possible technological perspective for future non-invasive glucose monitoring systems, while highlighting the need for further engineering optimization and clinical validation.

12. Conclusion

This review provides a comprehensive overview of current advances in non-invasive blood glucose monitoring by integrating scientific literature, bibliometric assessment, patent analysis, and recent developments in artificial intelligence. Together, these complementary perspectives highlight both the remarkable progress achieved and the scientific and technological challenges that continue to limit the clinical adoption of non-invasive glucose monitoring systems.

Among the technologies reviewed, near-infrared spectroscopy remains one of the most promising optical approaches for future wearable glucose monitoring. However, the evidence gathered throughout this review suggests that clinically reliable non-invasive glucose monitoring will require the combined advancement of optical sensing, intelligent data processing, personalized calibration, and rigorous clinical validation.

Finally, the patent-based prototype discussed in this review illustrates one possible technological perspective inspired by the current state of the art and the challenges identified throughout the literature. Although further engineering optimization and clinical evaluation remain necessary, the continued convergence of biomedical engineering, spectroscopy, artificial intelligence, and digital health technologies is expected to play a central role in the development of the next generation of non-invasive glucose monitoring systems.

Data and software availability

No data are associated with this article.

Acknowledgements

The authors gratefully acknowledge the Foundation for Research, Development and Innovation in Science and Engineering (FRDISI) for providing institutional support and a research environment that contributed to the preparation of this review.

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Non-Invasive Near-Infrared Blood Glucose Monitoring: Current Technologies, Patent Landscape, and Future Perspectives [version 1; peer review: awaiting peer review]

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