In vivo Microscopic Photoacoustic Spectroscopy for Non-Invasive Glucose Monitoring Invulnerable to Skin Secretion Products

Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monitor: A Pilot Study

BACKGROUND

Self-monitoring of glucose is important to the successful management of diabetes; however, existing monitoring methods require a degree of invasive measurement which can be unpleasant for users. This study investigates the accuracy of a noninvasive glucose monitoring system that analyses spectral variations in microwave signals.

METHODS

An open-label, pilot design study was conducted with four cohorts (N = 5/cohort). In each session, a dial-resonating sensor (DRS) attached to the wrist automatically collected data every 60 seconds, with a novel artificial intelligence (AI) model converting signal resonance output to a glucose prediction. Plasma glucose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean absolute relative difference (MARD) between the DRS and plasma glucose values.

RESULTS

Accurate plasma glucose predictions were obtained across all four cohorts using a random sampling procedure applied to the full four-cohort data set, with an average MARD of 10.3%. A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones.

CONCLUSIONS

These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.

Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

In order to efficiently and accurately monitor blood glucose concentration (BGC) synthetically influenced by various factors, quantitative blood glucose in vitro detection was studied using photoacoustic temporal spectroscopy (PTS) combined with a fusion deep neural network (fDNN). Meanwhile, a photoacoustic detection system influenced by five factors was set up, and 625 time-resolved photoacoustic signals of rabbit blood were collected under different influencing factors.In view of the sequence property for temporal signals, a dimension convolutional neural network (1DCNN) was established to extract features containing BGC. Through the parameters optimization and adjusting, the mean square error (MSE) of BGC was 0.51001 mmol/L for 125 testing sets. Then, due to the long-term dependence on temporal signals, a long short-term memory (LSTM) module was connected to enhance the prediction accuracy of BGC. With the optimal LSTM layers, the MSE of BGC decreased to 0.32104 mmol/L. To further improve prediction accuracy, a self-attention mechanism (SAM) module was coupled into and formed an fDNN model, i.e., 1DCNN-SAM-LSTM. The fDNN model not only combines the advantages of temporal expansion of 1DCNN and data long-term memory of LSTM, but also focuses on the learning of more important features of BGC. Comparison results show that the fDNN model outperforms the other six models. The determination coefficient of BGC for the testing set was 0.990, and the MSE reached 0.1432 mmol/L. Results demonstrate that PTS combined with 1DCNN-SAM-LSTM ensures higher accuracy of BGC under the synthetical influence of various factors, as well as greatly enhances the detection efficiency.

Design and Development of a Non-invasive Opto-Electronic Sensor for Blood Glucose Monitoring Using a Visible Light Source

Background The management of diabetes is critically dependent on the continuous monitoring of blood glucose levels. Contemporary approaches primarily utilize invasive methods, which often prove to be uncomfortable and can deter patient adherence. There is a pressing need for the development of novel strategies that improve patient compliance and simplify the process of glucose monitoring. Aim and objectives The primary objective of this research is to develop a non-invasive blood glucose monitoring system (NIBGMS) that offers a convenient alternative to conventional invasive methods. This study aims to demonstrate the feasibility and accuracy of using visible laser light at a wavelength of 650 nm for glucose monitoring and to address physiological and technical challenges associated with in vivo measurements. Methods Our approach involved the design of a device that exploits the quantitative relationship between glucose concentration and the refraction phenomena of laser light. The system was initially calibrated and tested using glucose solutions across a range of concentrations (25-500 mg/dL). To get around the problems that come up when people's skin and bodies are different, we combined an infrared (IR) transmitter (800 nm) and receiver that checks for changes in voltage, which are indicative of glucose levels. Results The prototype device was compared with a commercially available blood glucose monitor (Accu-Chek active machine; Roche Diabetes Care, Inc., Mumbai, India). The results demonstrated an average linearity of 95.7% relative to the Accu-Chek machine, indicating a high level of accuracy in the non-invasive measurement of glucose levels. Conclusions The findings suggest that our NIBGMS holds significant promise for clinical application. It reduces the discomfort associated with blood sampling and provides reliable measurements that are comparable to those of existing invasive methods. The successful development of this device paves the way for further commercial translation and could significantly improve the quality of life for individuals with diabetes, by facilitating easier and more frequent monitoring.

Augmenting authenticity for non-invasive in vivo detection of random blood glucose with photoacoustic spectroscopy using Kernel-based ridge regression

Photoacoustic Spectroscopy (PAS) is a potential method for the noninvasive detection of blood glucose. However random blood glucose testing can help to diagnose diabetes at an early stage and is crucial for managing and preventing complications with diabetes. In order to improve the diagnosis, control, and treatment of Diabetes Mellitus, an appropriate approach of noninvasive random blood glucose is required for glucose monitoring. A polynomial kernel-based ridge regression is proposed in this paper to detect random blood glucose accurately using PAS. Additionally, we explored the impact of the biological parameter BMI on the regulation of blood glucose, as it serves as the primary source of energy for the body's cells. The kernel function plays a pivotal role in kernel ridge regression as it enables the algorithm to capture intricate non-linear associations between input and output variables. Using a Pulsed Laser source with a wavelength of 905 nm, a noninvasive portable device has been developed to collect the Photoacoustic (PA) signal from a finger. A collection of 105 individual random blood glucose samples was obtained and their accuracy was assessed using three metrics: Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Mean Absolute Relative Difference (MARD). The respective values for these metrics were found to be 10.94 (mg/dl), 10.15 (mg/dl), and 8.86%. The performance of the readings was evaluated through Clarke Error Grid Analysis and Bland Altman Plot, demonstrating that the obtained readings outperformed the previously reported state-of-the-art approaches. To conclude the proposed IoT-based PAS random blood glucose monitoring system using kernel-based ridge regression is reported for the first time with more accuracy.

Non-invasive measurements of blood glucose levels by time-gating mid-infrared optoacoustic signals

Non-invasive glucose monitoring (NIGM) represents an attractive alternative to finger pricking for blood glucose assessment and management of diabetes. Nevertheless, current NIGM techniques do not measure glucose concentrations in blood but rely on indirect bulk measurement of glucose in interstitial fluid, where glucose is diluted and glucose dynamics are different from those in the blood, which impairs NIGM accuracy. Here we introduce a new biosensor, termed depth-gated mid-infrared optoacoustic sensor (DIROS), which allows, for the first time, non-invasive glucose detection in blood-rich volumes in the skin. DIROS minimizes interference caused by the stratum corneum and other superficial skin layers by time-gating mid-infrared optoacoustic signals to enable depth-selective localization of glucose readings in skin. In measurements on the ears of (female) mice, DIROS displays improved accuracy over bulk-tissue glucose measurements. Our work demonstrates how signal localization can improve NIGM accuracy and positions DIROS as a holistic approach, with high translational potential, that addresses a key limitation of current NIGM methods.

Ultraflexible Wireless Imager Integrated with Organic Circuits for Broadband Infrared Thermal Analysis

Flexible imagers are currently under intensive development as versatile optical sensor arrays, designed to capture images of surfaces and internals, irrespective of their shape. A significant challenge in developing flexible imagers is extending their detection capabilities to encompass a broad spectrum of infrared light, particularly terahertz (THz) light at room temperature. This advancement is crucial for thermal and biochemical applications. In this study, a flexible infrared imager is designed using uncooled carbon nanotube (CNT) sensors and organic circuits. The CNT sensors, fabricated on ultrathin 2.4 µm substrates, demonstrate enhanced sensitivity across a wide infrared range, spanning from near-infrared to THz wavelengths. Moreover, they retain their characteristics under bending and crumpling. The design incorporates light-shielded organic transistors and circuits, functioning reliably under light irradiation, and amplifies THz detection signals by a factor of 10. The integration of both CNT sensors and shielded organic transistors into an 8 × 8 active-sensor matrix within the imager enables sequential infrared imaging and nondestructive assessment for heat sources and in-liquid chemicals through wireless communication systems. The proposed imager, offering unique functionality, shows promise for applications in biochemical analysis and soft robotics.

Review of Noninvasive Continuous Glucose Monitoring in Diabetics

For diabetics, taking regular blood glucose measurements is crucial. However, traditional blood glucose monitoring methods are invasive and unfriendly to diabetics. Recent studies have proposed a biofluid-based glucose sensing technique that creatively combines wearable devices with noninvasive glucose monitoring technology to enhance diabetes management. This is a revolutionary advance in the diagnosis and management of diabetes, reflects the thoughtful modernization of medicine, and promotes the development of digital medicine. This paper reviews the research progress of noninvasive continuous blood glucose monitoring (CGM), with a focus on the biological liquids that replace blood in monitoring systems, the technical principles of continuous noninvasive glucose detection, and the output and calibration of sensor signals. In addition, the existing limits of noninvasive CGM systems and prospects for the future are discussed. This work serves as a resource for further promoting the development of noninvasive CGM systems.

On-body non-invasive glucose monitoring sensor based on high figure of merit (FoM) surface plasmonic microwave resonator

High-figure of merit (FoM) plasmonic microwave resonator is researched as a non-invasive on-body sensor to monitor the human body's blood glucose variation rate in adults for biomedical applications, e.g., diabetic patients. The resonance frequencies of the proposed sensor are measured to be around [Formula: see text] GHz and [Formula: see text] GHz over the frequency band of DC to 6GHz which are suitable for monitoring interstitial fluid (ISF) changing rate. The [Formula: see text] sensor is experimentally wrapped on the human body arm to monitor the blood glucose changing rate via amplitude and frequency variations of the sensor. Amplitude variation and frequency shift are measured to be around 7 dB and 30 MHz, respectively. The measured results demonstrate the high precision of the proposed approach to depict a valid diagram for glucose changing rate due to good impedance matching of the designed microwave sensor and human body. The sensor is shown to enhance the sensitivity by a factor of 5 compared to the conventional ones.

Optoacoustic classification of diabetes mellitus with the synthetic impacts via optimized neural networks

A highly accurate classification of diabetes mellitus (DM) with the synthetic impacts of several variables is first studied via optoacoustic technology in this work. For this purpose, an optoacoustic measurement apparatus of blood glucose is built, and the optoacoustic signals and peak-peak values for 625 cases of in vitro rabbit blood are obtained. The results show that although the single impact of five variables are obtained, the precise classification of DM is limited because of the synthetic impacts. Based on clinical standards, different levels of blood glucose corresponding to hypoglycaemia, normal, slight diabetes, moderate diabetes and severe diabetes are employed. Then, a wavelet neural network (WNN) is utilized to establish a classification model of DM severity. The classification accuracy is 94.4 % for the testing blood samples. To enhance the classification accuracy, particle swarm optimization (PSO) and quantum-behaved particle swarm optimization (QPSO) are successively utilized to optimize WNN, and accuracy is enhanced to 98.4 % and 100 %, respectively. It is demonstrated from comparison between several algorithms that optoacoustic technology united with the QPSO-optimized WNN algorithm can achieve precise classification of DM with synthetic impacts.

Noninvasive Glucose Sensing In Vivo

Blood glucose monitoring is an essential aspect of disease management for individuals with diabetes. Unfortunately, traditional methods require collecting a blood sample and thus are invasive and inconvenient. Recent developments in minimally invasive continuous glucose monitors have provided a more convenient alternative for people with diabetes to track their glucose levels 24/7. Despite this progress, many challenges remain to establish a noninvasive monitoring technique that works accurately and reliably in the wild. This review encompasses the current advancements in noninvasive glucose sensing technology in vivo, delves into the common challenges faced by these systems, and offers an insightful outlook on existing and future solutions.

A flexible optoacoustic blood 'stethoscope' for noninvasive multiparametric cardiovascular monitoring

Quantitative and multiparametric blood analysis is of great clinical importance in cardiovascular disease diagnosis. Although there are various methods to extract blood information, they often require invasive procedures, lack continuity, involve bulky instruments, or have complicated testing procedures. Flexible sensors can realize on-skin assessment of several vital signals, but generally exhibit limited function to monitor blood characteristics. Here, we report a flexible optoacoustic blood 'stethoscope' for noninvasive, multiparametric, and continuous cardiovascular monitoring, without requiring complicated procedures. The optoacoustic blood 'stethoscope' features the light delivery elements to illuminate blood and the piezoelectric acoustic elements to capture light-induced acoustic waves. We show that the optoacoustic blood 'stethoscope' can adhere to the skin for continuous and non-invasive in-situ monitoring of multiple cardiovascular biomarkers, including hypoxia, intravascular exogenous agent concentration decay, and hemodynamics, which can be further visualized with a tailored 3D algorithm. Demonstrations on both in-vivo animal trials and human subjects highlight the optoacoustic blood 'stethoscope''s potential for cardiovascular disease diagnosis and prediction.

Niche preclinical and clinical applications of photoacoustic imaging with endogenous contrast

In the past decade, photoacoustic (PA) imaging has attracted a great deal of popularity as an emergent diagnostic technology owing to its successful demonstration in both preclinical and clinical arenas by various academic and industrial research groups. Such steady growth of PA imaging can mainly be attributed to its salient features, including being non-ionizing, cost-effective, easily deployable, and having sufficient axial, lateral, and temporal resolutions for resolving various tissue characteristics and assessing the therapeutic efficacy. In addition, PA imaging can easily be integrated with the ultrasound imaging systems, the combination of which confers the ability to co-register and cross-reference various features in the structural, functional, and molecular imaging regimes. PA imaging relies on either an endogenous source of contrast (e.g., hemoglobin) or those of an exogenous nature such as nano-sized tunable optical absorbers or dyes that may boost imaging contrast beyond that provided by the endogenous sources. In this review, we discuss the applications of PA imaging with endogenous contrast as they pertain to clinically relevant niches, including tissue characterization, cancer diagnostics/therapies (termed as theranostics), cardiovascular applications, and surgical applications. We believe that PA imaging's role as a facile indicator of several disease-relevant states will continue to expand and evolve as it is adopted by an increasing number of research laboratories and clinics worldwide.

Review of imaging test phantoms

Significance

Photoacoustic tomography has emerged as a prominent medical imaging technique that leverages its hybrid nature to provide deep penetration, high resolution, and exceptional optical contrast with notable applications in early cancer detection, functional brain imaging, drug delivery monitoring, and guiding interventional procedures. Test phantoms are pivotal in accelerating technology development and commercialization, specifically in photoacoustic (PA) imaging, and can be optimized to achieve significant advancements in PA imaging capabilities.

Aim

The analysis of material properties, structural characteristics, and manufacturing methodologies of test phantoms from existing imaging technologies provides valuable insights into their applicability to PA imaging. This investigation enables a deeper understanding of how phantoms can be effectively employed in the context of PA imaging.

Approach

Three primary categories of test phantoms (simple, intermediate, and advanced) have been developed to differentiate complexity and manufacturing requirements. In addition, four sub-categories (tube/channel, block, test target, and naturally occurring phantoms) have been identified to encompass the structural variations within these categories, resulting in a comprehensive classification system for test phantoms.

Results

Based on a thorough examination of literature and studies on phantoms in various imaging modalities, proposals have been put forth for the development of multiple PA-capable phantoms, encompassing considerations related to the material composition, structural design, and specific applications within each sub-category.

Conclusions

The advancement of novel and sophisticated test phantoms within each sub-category is poised to foster substantial progress in both the commercialization and development of PA imaging. Moreover, the continued refinement of test phantoms will enable the exploration of new applications and use cases for PA imaging.

Progress and Perspectives of Mid-Infrared Photoacoustic Spectroscopy for Non-Invasive Glucose Detection

The prevalence of diabetes is rapidly increasing worldwide and can lead to a range of severe health complications that have the potential to be life-threatening. Patients need to monitor and control blood glucose levels as it has no cure. The development of non-invasive techniques for the measurement of blood glucose based on photoacoustic spectroscopy (PAS) has advanced tremendously in the last couple of years. Among them, PAS in the mid-infrared (MIR) region shows great promise as it shows the distinct fingerprint region for glucose. However, two problems are generally encountered when it is applied to monitor real samples for in vivo measurements in this MIR spectral range: (i) low penetration depth of MIR light into the human skin, and (ii) the effect of other interfering components in blood, which affects the selectivity of the detection system. This review paper systematically describes the basics of PAS in the MIR region, along with recent developments, technical challenges, and data analysis strategies, and proposes improvements for the detection sensitivity of glucose concentration in human bodies. It also highlights the recent trends of incorporating machine learning (ML) to enhance the detection sensitivity of the overall system. With further optimization of the experimental setup and incorporation of ML, this PAS in the MIR spectral region could be a viable solution for the non-invasive measurement of blood glucose in the near future.

The sound of blood: photoacoustic imaging in blood analysis

Blood analysis is a ubiquitous and critical aspect of modern medicine. Analyzing blood samples requires invasive techniques, various testing systems, and samples are limited to relatively small volumes. Photoacoustic imaging (PAI) is a novel imaging modality that utilizes non-ionizing energy that shows promise as an alternative to current methods. This paper seeks to review current applications of PAI in blood analysis for clinical use. Furthermore, we discuss obstacles to implementation and future directions to overcome these challenges. Firstly, we discuss three applications to cellular analysis of blood: sickle cell, bacteria, and circulating tumor cell detection. We then discuss applications to the analysis of blood plasma, including glucose detection and anticoagulation quantification. As such, we hope this article will serve as inspiration for PAI's potential application in blood analysis and prompt further studies to ultimately implement PAI into clinical practice.

Dual quantum cascade lasers for noninvasive glucose detection using photoacoustic spectroscopy

The combination of mid-infrared and photoacoustic spectroscopy has shown promising developments as a substitute for invasive glucose detection technology. A dual single-wavelength quantum cascade laser system has been developed using photoacoustic spectroscopy for noninvasive glucose monitoring. Biomedical skin phantoms with similar properties to human skin have been prepared with blood components at different glucose concentrations as test models for the setup. The detection sensitivity of the system has been improved to ± 12.5 mg/dL in the hyperglycemia blood glucose ranges. An ensemble machine learning classifier has been developed to predict the glucose level in the presence of blood components. The model, which was trained with 72,360 unprocessed datasets, achieved a 96.7% prediction accuracy with 100% of the predicted data located in zones A and B of Clarke's error grid analysis. These findings fulfill both the US Food and Drug Administration and Health Canada requirements for glucose monitors.

Glucose diagnosis system combining machine learning and NIR photoacoustic multispectral using a low power CW laser

Non-invasive, portable, economical, dynamic blood glucose monitoring device has become a functional requirement for diabetes in his regulating entire life. In a photoacoustic (PA) multispectral near-infrared diagnosis system, the glucose in aqueous solutions was excited by low power (order of milliwatts) CW laser whose wavelengths were from 1500 to 1630 nm. The glucose in aqueous solutions to be analyzed was contained within the photoacoustic cell (PAC). The PA multispectral signals were measured using a piezoelectric detector, and then the voltage signals from the piezoelectric detector were amplified with a precision Lock-in Amplifier (MFLI500K). The continuously tunable lasers were used to verify the various influencing factors of the PA signal, and the PA spectrum of the glucose solution was examined. Subsequently, six wavelengths with high power were selected at approximately equal intervals from 1500 to 1630 nm, and the gaussian process regression of the quadratic rational kernel was used to collect data through these wavelengths to predict the glucose concentration. The experimental results showed that the near-infrared PA multispectral diagnosis system could be engineered for the prediction of the glucose level (more than 92%, zone A of Clarke Error Grid). Subsequently, the model trained with glucose solution was used to predict serum glucose. With the increase of serum glucose content, the prediction results of the model also showed a high linear relationship, indicating that the photoacoustic method was sensitive to the detection of glucose concentration changes. The results of our study have the potential to not only better develop the PA blood glucose meter but also extend the viability into the detection of otherwise blood components.

Accurate Post-Calibration Predictions for Noninvasive Glucose Measurements in People Using Confocal Raman Spectroscopy

In diabetes prevention and care, invasiveness of glucose measurement impedes efficient therapy and hampers the identification of people at risk. Lack of calibration stability in non-invasive technology has confined the field to short-term proof of principle. Addressing this challenge, we demonstrate the first practical use of a Raman-based and portable non-invasive glucose monitoring device used for at least 15 days following calibration. In a home-based clinical study involving 160 subjects with diabetes, the largest of its kind to our knowledge, we find that the measurement accuracy is insensitive to age, sex, and skin color. A subset of subjects with type 2 diabetes highlights promising real-life results with 99.8% of measurements within A + B zones in the consensus error grid and a mean absolute relative difference of 14.3%. By overcoming the problem of calibration stability, we remove the lingering uncertainty about the practical use of non-invasive glucose monitoring, boding a new, non-invasive era in diabetes monitoring.

Comparative assessment of blood glucose monitoring techniques: a review

Monitoring blood glucose levels is a vital indicator of diabetes mellitus management. The mainstream techniques of glucometers are invasive, painful, expensive, intermittent, and time-consuming. The ever-increasing number of global diabetic patients urges the development of alternative non-invasive glucose monitoring techniques. Recent advances in electrochemical biosensors, biomaterials, wearable sensors, biomedical signal processing, and microfabrication technologies have led to significant research and ideas in elevating the patient's life quality. This review provides up-to-date information about the available technologies and compares the advantages and limitations of invasive and non-invasive monitoring techniques. The scope of measuring glucose concentration in other bio-fluids such as interstitial fluid (ISF), tears, saliva, and sweat are also discussed. The high accuracy level of invasive methods in measuring blood glucose concentrations gives them superiority over other methods due to lower average absolute error between the detected glucose concentration and reference values. Whereas minimally invasive, and non-invasive techniques have the advantages of continuous and pain-free monitoring. Various blood glucose monitoring techniques have been evaluated based on their correlation to blood, patient-friendly, time efficiency, cost efficiency, and accuracy. Finally, this review also compares the currently available glucose monitoring devices in the market.

Recent Advances in Contrast-Enhanced Photoacoustic Imaging: Overcoming the Physical and Practical Challenges

For decades now, photoacoustic imaging (PAI) has been investigated to realize its potential as a niche biomedical imaging modality. Despite its highly desirable optical contrast and ultrasonic spatiotemporal resolution, PAI is challenged by such physical limitations as a low signal-to-noise ratio (SNR), diminished image contrast due to strong optical attenuation, and a lower-bound on spatial resolution in deep tissue. In addition, contrast-enhanced PAI has faced practical limitations such as insufficient cell-specific targeting due to low delivery efficiency and difficulties in developing clinically translatable agents. Identifying these limitations is essential to the continuing expansion of the field, and substantial advances in developing contrast-enhancing agents, complemented by high-performance image acquisition systems, have synergistically dealt with the challenges of conventional PAI. This review covers the past four years of research on pushing the physical and practical challenges of PAI in terms of SNR/contrast, spatial resolution, targeted delivery, and clinical application. Promising strategies for dealing with each challenge are reviewed in detail, and future research directions for next generation contrast-enhanced PAI are discussed.

A photoacoustic patch for three-dimensional imaging of hemoglobin and core temperature

Electronic patches, based on various mechanisms, allow continuous and noninvasive monitoring of biomolecules on the skin surface. However, to date, such devices are unable to sense biomolecules in deep tissues, which have a stronger and faster correlation with the human physiological status than those on the skin surface. Here, we demonstrate a photoacoustic patch for three-dimensional (3D) mapping of hemoglobin in deep tissues. This photoacoustic patch integrates an array of ultrasonic transducers and vertical-cavity surface-emitting laser (VCSEL) diodes on a common soft substrate. The high-power VCSEL diodes can generate laser pulses that penetrate >2 cm into biological tissues and activate hemoglobin molecules to generate acoustic waves, which can be collected by the transducers for 3D imaging of the hemoglobin with a high spatial resolution. Additionally, the photoacoustic signal amplitude and temperature have a linear relationship, which allows 3D mapping of core temperatures with high accuracy and fast response. With access to biomolecules in deep tissues, this technology adds unprecedented capabilities to wearable electronics and thus holds significant implications for various applications in both basic research and clinical practice.

A Review of Emerging Electromagnetic-Acoustic Sensing Techniques for Healthcare Monitoring

Conventional electromagnetic (EM) sensing techniques such as radar and LiDAR are widely used for remote sensing, vehicle applications, weather monitoring, and clinical monitoring. Acoustic techniques such as sonar and ultrasound sensors are also used for consumer applications, such as ranging and in vivo medical/healthcare applications. It has been of long-term interest to doctors and clinical practitioners to realize continuous healthcare monitoring in hospitals and/or homes. Physiological and biopotential signals in real-time serve as important health indicators to predict and prevent serious illness. Emerging electromagnetic-acoustic (EMA) sensing techniques synergistically combine the merits of EM sensing with acoustic imaging to achieve comprehensive detection of physiological and biopotential signals. Further, EMA enables complementary fusion sensing for challenging healthcare settings, such as real-world long-term monitoring of treatment effects at home or in remote environments. This article reviews various examples of EMA sensing instruments, including implementation, performance, and application from the perspectives of circuits to systems. The novel and significant applications to healthcare are discussed. Three types of EMA sensors are presented: (1) Chip-based radar sensors for health status monitoring, (2) Thermo-acoustic sensing instruments for biomedical applications, and (3) Photoacoustic (PA) sensing and imaging systems, including dedicated reconstruction algorithms were reviewed from time-domain, frequency-domain, time-reversal, and model-based solutions. The future of EMA techniques for continuous healthcare with enhanced accuracy supported by artificial intelligence (AI) is also presented.

Glucose emission spectra through mid-infrared passive spectroscopic imaging of the wrist for non-invasive glucose sensing

Non-invasive blood glucose sensing can be achieved using mid-infrared spectroscopy, although no practical device based on this method has yet been developed. Here, we propose mid-infrared passive spectroscopic imaging for glucose measurements from a distance. Spectroscopic imaging of thermal radiation from the human body enabled, for the first time in the world, the detection of glucose-induced luminescence from a distance. In addition, glucose emission spectra of the wrist acquired at regular intervals up to 60 min showed that there was a strong correlation between the glucose emission intensity and blood glucose level measured using an invasive sensor. Thus, the new technology proposed here is expected to be applied to real-time monitoring of diabetic patients to detect hypoglycemic attacks during sleep and to detect hyperglycemia in a population. Moreover, this technology could lead to innovations that would make it possible to remotely measure a variety of substances.

In Silico Investigation of SNR and Dermis Sensitivity for Optimum Dual-Channel Near-Infrared Glucose Sensor Designs for Different Skin Colors

Diabetes is a serious health condition that requires patients to regularly monitor their blood glucose level, making the development of practical, compact, and non-invasive techniques essential. Optical glucose sensors-and, specifically, NIR sensors-have the advantages of being non-invasive, compact, inexpensive, and user-friendly devices. However, these sensors have low accuracy and are yet to be adopted by healthcare providers. In our previous work, we introduced a non-invasive dual-channel technique for NIR sensors, in which a long channel is utilized to measure the glucose level in the inner skin (dermis) layer, while a short channel is used to measure the noise signal of the superficial skin (epidermis) layer. In this work, we investigated the use of dual-NIR channels for patients with different skin colors (i.e., having different melanin concentrations). We also adopted a Monte Carlo simulation model that takes into consideration the differences between different skin layers, in terms of blood content, water content, melanin concentration in the epidermis layer, and skin optical proprieties. On the basis of the signal-to-noise ratio, as well as the sensitivities of both the epidermis and dermis layers, we suggest the selection of wavelengths and source-to-detector separation for optimal NIR channels under different skin melanin concentrations. This work facilitates the improved design of a compact and non-invasive NIR glucose sensor that can be utilized by patients with different skin colors.

Is Raman the best strategy towards the development of non-invasive continuous glucose monitoring devices for diabetes management?

Diabetes has no well-established cure; thus, its management is critical for avoiding severe health complications involving multiple organs. This requires frequent glycaemia monitoring, and the gold standards for this are fingerstick tests. During the last decades, several blood-withdrawal-free platforms have been being studied to replace this test and to improve significantly the quality of life of people with diabetes (PWD). Devices estimating glycaemia level targeting blood or biofluids such as tears, saliva, breath and sweat, are gaining attention; however, most are not reliable, user-friendly and/or cheap. Given the complexity of the topic and the rise of diabetes, a careful analysis is essential to track scientific and industrial progresses in developing diabetes management systems. Here, we summarize the emerging blood glucose level (BGL) measurement methods and report some examples of devices which have been under development in the last decades, discussing the reasons for them not reaching the market or not being really non-invasive and continuous. After discussing more in depth the history of Raman spectroscopy-based researches and devices for BGL measurements, we will examine if this technique could have the potential for the development of a user-friendly, miniaturized, non-invasive and continuous blood glucose-monitoring device, which can operate reliably, without inter-patient variability, over sustained periods.

Nanozyme-based colorimetric biosensor with a systemic quantification algorithm for noninvasive glucose monitoring

Diabetes mellitus accompanies an abnormally high glucose level in the bloodstream. Early diagnosis and proper glycemic management of blood glucose are essential to prevent further progression and complications. Biosensor-based colorimetric detection has progressed and shown potential in portable and inexpensive daily assessment of glucose levels because of its simplicity, low-cost, and convenient operation without sophisticated instrumentation. Colorimetric glucose biosensors commonly use natural enzymes that recognize glucose and chromophores that detect enzymatic reaction products. However, many natural enzymes have inherent defects, limiting their extensive application. Recently, nanozyme-based colorimetric detection has drawn attention due to its merits including high sensitivity, stability under strict reaction conditions, flexible structural design with low-cost materials, and adjustable catalytic activities. This review discusses various nanozyme materials, colorimetric analytic methods and mechanisms, recent machine learning based analytic methods, quantification systems, applications and future directions for monitoring and managing diabetes.

Multichannel Acoustic Spectroscopy of the Human Body for Inviolable Biometric Authentication

Specific features of the human body, such as fingerprint, iris, and face, are extensively used in biometric authentication. Conversely, the internal structure and material features of the body have not been explored extensively in biometrics. Bioacoustics technology is suitable for extracting information about the internal structure and biological and material characteristics of the human body. Herein, we report a biometric authentication method that enables multichannel bioacoustic signal acquisition with a systematic approach to study the effects of selectively distilled frequency features, increasing the number of sensing channels with respect to multiple fingers. The accuracy of identity recognition according to the number of sensing channels and the number of selectively chosen frequency features was evaluated using exhaustive combination searches and forward-feature selection. The technique was applied to test the accuracy of machine learning classification using 5,232 datasets from 54 subjects. By optimizing the scanning frequency and sensing channels, our method achieved an accuracy of 99.62%, which is comparable to existing biometric methods. Overall, the proposed biometric method not only provides an unbreakable, inviolable biometric but also can be applied anywhere in the body and can substantially broaden the use of biometrics by enabling continuous identity recognition on various body parts for biometric identity authentication.

Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology—A Review

The past few decades have seen ongoing development of continuous glucose monitoring (CGM) systems that are noninvasive and accurately measure blood glucose levels. The conventional finger-prick method, though accurate, is not feasible for use multiple times a day, as it is painful and test strips are expensive. Although minimally invasive and noninvasive CGM systems have been introduced into the market, they are expensive and require finger-prick calibrations. As the diabetes trend is high in low- and middle-income countries, a cost-effective and easy-to-use noninvasive glucose monitoring device is the need of the hour. This review paper briefly discusses the noninvasive glucose measuring technologies and their related research work. The technologies discussed are optical, transdermal, and enzymatic. The paper focuses on Near Infrared (NIR) technology and NIR Photoplethysmography (PPG) for blood glucose prediction. Feature extraction from PPG signals and glucose prediction with machine learning methods are discussed. The review concludes with key points and insights for future development of PPG NIR-based blood glucose monitoring systems.

A Single Wavelength Mid-Infrared Photoacoustic Spectroscopy for Noninvasive Glucose Detection Using Machine Learning

According to the International Diabetes Federation, 530 million people worldwide have diabetes, with more than 6.7 million reported deaths in 2021. Monitoring blood glucose levels is essential for individuals with diabetes, and developing noninvasive monitors has been a long-standing aspiration in diabetes management. The ideal method for monitoring diabetes is to obtain the glucose concentration level with a fast, accurate, and pain-free measurement that does not require blood drawing or a surgical operation. Multiple noninvasive glucose detection techniques have been developed, including bio-impedance spectroscopy, electromagnetic sensing, and metabolic heat conformation. Nevertheless, reliability and consistency challenges were reported for these methods due to ambient temperature and environmental condition sensitivity. Among all the noninvasive glucose detection techniques, optical spectroscopy has rapidly advanced. A photoacoustic system has been developed using a single wavelength quantum cascade laser, lasing at a glucose fingerprint of 1080 cm-1 for noninvasive glucose monitoring. The system has been examined using artificial skin phantoms, covering the normal and hyperglycemia blood glucose ranges. The detection sensitivity of the system has been improved to ±25 mg/dL using a single wavelength for the entire range of blood glucose. Machine learning has been employed to detect glucose levels using photoacoustic spectroscopy in skin samples. Ensemble machine learning models have been developed to measure glucose concentration using classification techniques. The model has achieved a 90.4% prediction accuracy with 100% of the predicted data located in zones A and B of Clarke's error grid analysis. This finding fulfills the US Food and Drug Administration requirements for glucose monitors.

A machine learning-based on-demand sweat glucose reporting platform

Diabetes is a chronic endocrine disease that occurs due to an imbalance in glucose levels and altering carbohydrate metabolism. It is a leading cause of morbidity, resulting in a reduced quality of life even in developed societies, primarily affected by a sedentary lifestyle and often leading to mortality. Keeping track of blood glucose levels noninvasively has been made possible due to diverse breakthroughs in wearable sensor technology coupled with holistic digital healthcare. Efficient glucose management has been revolutionized by the development of continuous glucose monitoring sensors and wearable, non/minimally invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1-5 min. This paper presents a highly novel and completely non-invasive sweat sensor platform technology that can measure and report glucose concentrations from passively expressed human eccrine sweat using electrochemical impedance spectroscopy and affinity capture probe functionalized sensor surfaces. The sensor samples 1-5 µL of sweat from the wearer every 1-5 min and reports sweat glucose from a machine learning algorithm that samples the analytical reference values from the electrochemical sweat sensor. These values are then converted to continuous time-varying signals using the interpolation methodology. Supervised machine learning, the decision tree regression algorithm, shows the goodness of fit R² of 0.94 was achieved with an RMSE value of 0.1 mg/dL. The output of the model was tested on three human subject datasets. The results were able to capture the glucose progression trend correctly. Sweet sensor platform technology demonstrates a dynamic response over the physiological sweat glucose range of 1-4 mg/dL measured from 3 human subjects. The technology described in the manuscript shows promise for real-time biomarkers such as glucose reporting from passively expressed human eccrine sweat.

Noninvasive Spectroscopic Detection of Blood Glucose and Analysis of Clinical Research Status

Frequent measurement of blood glucose concentration in diabetic patients is an important means for diabetes control. Blood glucose monitoring with noninvasive detection technology can not only avoid the pain of patients and eliminate the harm of some biological materials for measuring glucose in vivo but also improve the frequency of detection, so as to control blood glucose concentration more closely. Traditional blood glucose detection methods are invasive and have some limitations. In this study, the significance of noninvasive blood glucose testing was analyzed and was pointed out that noninvasive blood glucose testing can monitor the blood glucose concentration of patients and relieve the pain of patients. Then, this study analyzed the spectral detection methods of noninvasive blood glucose, including conservation of energy metabolism, near infrared spectroscopy, and other spectral detection methods. Finally, this study made a comprehensive analysis of the domestic and international clinical application of noninvasive glucose spectrum monitoring and summarized the clinical application status of noninvasive glucose spectrum monitoring.

Non-Invasive Monitoring of Human Health by Photoacoustic Spectroscopy

For certain diseases, the continuous long-term monitoring of the physiological condition is crucial. Therefore, non-invasive monitoring methods have attracted widespread attention in health care. This review aims to discuss the non-invasive monitoring technologies for human health based on photoacoustic spectroscopy. First, the theoretical basis of photoacoustic spectroscopy and related devices are reported. Furthermore, this article introduces the monitoring methods for blood glucose, blood oxygen, lipid, and tumors, including differential continuous-wave photoacoustic spectroscopy, microscopic photoacoustic spectroscopy, mid-infrared photoacoustic detection, wavelength-modulated differential photoacoustic spectroscopy, and others. Finally, we present the limitations and prospects of photoacoustic spectroscopy.

Non-invasively accuracy enhanced blood glucose sensor using shallow dense neural networks with NIR monitoring and medical features

Non-invasive and accurate method for continuous blood glucose monitoring, the self-testing of blood glucose is in quest for better diagnosis, control and the management of diabetes mellitus (DM). Therefore, this study reports a multiple photonic band near-infrared (mbNIR) sensor augmented with personalized medical features (PMF) in Shallow Dense Neural Networks (SDNN) for the precise, inexpensive and pain free blood glucose determination. Datasets collected from 401 blood samples were randomized and trained with ten-fold validation. Additionally, a cohort of 234 individuals not included in the model training set were investigated to evaluate the performance of the model. The model achieved the accuracy of 97.8% along with 96.0% precision, 94.8% sensitivity and 98.7% specificity for DM classification based on a diagnosis threshold of 126 mg/dL for diabetes in fasting blood glucose. For non-invasive real-time blood glucose monitoring, the model exhibited ± 15% error with 95% confidence interval and the detection limit of 60–400 mg/dL, as validated with the standard hexokinase enzymatic method for glucose estimation. In conclusion, this proposed mbNIR based SDNN model with PMF is highly accurate and computationally cheaper compared to similar previous works using complex neural network. Some groups proposed using complicated mixed types of sensors to improve noninvasive glucose prediction accuracy; however, the accuracy gain over the complexity and costs of the systems harvested is still in questioned (Geng et al. in Sci Rep 7:12650, 2017). None of previous works report on accuracy enhancement of NIR/NN using PMF. Therefore, the proposed SDNN over PMF/mbNIR is an extremely promising candidate for the non-invasive real-time blood glucose monitoring with less complexity and pain-free.

Comprehensive Review on Wearable Sweat-Glucose Sensors for Continuous Glucose Monitoring

The incidence of diabetes is increasing at an alarming rate, and regular glucose monitoring is critical in order to manage diabetes. Currently, glucose in the body is measured by an invasive method of blood sugar testing. Blood glucose (BG) monitoring devices measure the amount of sugar in a small sample of blood, usually drawn from pricking the fingertip, and placed on a disposable test strip. Therefore, there is a need for non-invasive continuous glucose monitoring, which is possible using a sweat sensor-based approach. As sweat sensors have garnered much interest in recent years, this study attempts to summarize recent developments in non-invasive continuous glucose monitoring using sweat sensors based on different approaches with an emphasis on the devices that can potentially be integrated into a wearable platform. Numerous research entities have been developing wearable sensors for continuous blood glucose monitoring, however, there are no commercially viable, non-invasive glucose monitors on the market at the moment. This review article provides the state-of-the-art in sweat glucose monitoring, particularly keeping in sight the prospect of its commercialization. The challenges relating to sweat collection, sweat sample degradation, person to person sweat amount variation, various detection methods, and their glucose detection sensitivity, and also the commercial viability are thoroughly covered.

Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning

Blood glucose (BG) concentration monitoring is essential for controlling complications arising from diabetes, as well as digital management of the disease. At present, finger-prick glucometers are widely used to measure BG concentrations. In consideration of the challenges of invasive BG concentration measurements involving pain, risk of infection, expense, and inconvenience, we propose a noninvasive BG concentration detection method based on the conservation of energy metabolism. In this study, a multisensor integrated detection probe was designed and manufactured by 3D-printing technology to be worn on the wrist. Two machine-learning algorithms were also applied to establish the regression model for predicting BG concentrations. The results showed that the back-propagation neural network model produced better performance than the multivariate polynomial regression model, with a mean absolute relative difference and correlation coefficient of 5.453% and 0.936, respectively. Here, about 98.413% of the predicted values were within zone A of the Clarke error grid. The above results proved the potential of our method and device for noninvasive glucose concentration detection from the human wrist.

A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

Performance Enhancement of Opened Resonance Photoacoustic Cells Based on Three Dimensional Topology Optimization

Photoacoustic (PA) spectroscopy techniques enable the detection of trace substances. However, lower threshold detection requirements are increasingly common in practical applications. Thus, we propose a systematic geometry topology optimization approach on a PA cell to enhance the intensity of its detection signal. The model of topology optimization and pressure acoustics in the finite element method was exploited to construct a PA cell and then acquire the optimal structure. In the assessment, a thermo-acoustic model was constructed to properly simulate the frequency response over the range of 0–70 kHz and the temperature field distribution. The simulation results revealed that the acoustic gain of the optimized cell was 2.7 and 1.3 times higher than conventional cells near 25 and 52 kHz, respectively. Moreover, the optimized PA cell achieved a lower threshold detection over a wide frequency range. Ultimately, this study paves a new way for designing and optimizing the geometry of multifarious high-sensitivity PA sensors.

Innovative IoT Solutions and Wearable Sensing Systems for Monitoring Human Biophysical Parameters: A Review

Digital and information technologies are heavily pervading several aspects of human activities, improving our life quality. Health systems are undergoing a real technological revolution, radically changing how medical services are provided, thanks to the wide employment of the Internet of Things (IoT) platforms supporting advanced monitoring services and intelligent inferring systems. This paper reports, at first, a comprehensive overview of innovative sensing systems for monitoring biophysical and psychophysical parameters, all suitable for integration with wearable or portable accessories. Wearable devices represent a headstone on which the IoT-based healthcare platforms are based, providing capillary and real-time monitoring of patient’s conditions. Besides, a survey of modern architectures and supported services by IoT platforms for health monitoring is presented, providing useful insights for developing future healthcare systems. All considered architectures employ wearable devices to gather patient parameters and share them with a cloud platform where they are processed to provide real-time feedback. The reported discussion highlights the structural differences between the discussed frameworks, from the point of view of network configuration, data management strategy, feedback modality, etc.

Optoacoustic Imaging of Glucagon-like Peptide-1 Receptor with a Near-Infrared Exendin-4 Analog

Limitations in current imaging tools have long challenged the imaging of small pancreatic islets in animal models. Here, we report the first development and in vivo validation testing of a broad-spectrum and high-absorbance near-infrared optoacoustic contrast agent, E4x12-Cy7. Our near-infrared tracer is based on the amino acid sequence of exendin-4 and targets the glucagon-like peptide-1 receptor (GLP-1R). Cell assays confirmed that E4x12-Cy7 has a high-binding affinity (dissociation constant, Kd, 4.6 ± 0.8 nM). Using the multispectral optoacoustic tomography, we imaged E4x12-Cy7 and optoacoustically visualized β-cell insulinoma xenografts in vivo for the first time. In the future, similar optoacoustic tracers that are specific for β-cells and combines optoacoustic and fluorescence imaging modalities could prove to be important tools for monitoring the pancreas for the progression of diabetes.

Time-domain photoacoustic waveform analysis for glucose measurement

Photoacoustic (PA) effect is the product of light-ultrasound interactions and its time-domain waveform contains rich information. Besides optical absorption, the PA waveform inherently consists of other mechanical and thermal properties of the sample. They also have correlation with the target composition but have not been utilized in conventional PA spectroscopy. In this article, we propose a new concept named time-domain photoacoustic waveform spectroscopy (tPAWS) for chemical component quantification. It employs multiple variables inherently contained in the PA waveform excited by a single wavelength laser to extract informative features. The demonstration of glucose measurement in human blood serum (HBS) shows superior sensitivity and accuracy enhancement, compared to conventional amplitude-based PA measurement and NIR spectroscopy. Thanks to the sensitivity and accuracy of tPAWS, multiple wavelength sources and complex instrumentation used in conventional spectroscopic sensing methods can be avoided. TPAWS, as a novel physics-inspired sensing method, shows great potential for complementing or surpassing the current spectroscopic methods as a new sensing technique for chemical analysis.

Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications

Diabetes is a major public health challenge affecting more than 451 million people. Physiological and experimental factors influence the accuracy of non-invasive glucose monitoring, and these need to be overcome before replacing the finger prick method. Also, the suitable employment of machine learning techniques can significantly improve the accuracy of glucose predictions. One aim of this study is to use light sources with multiple wavelengths to enhance the sensitivity and selectivity of glucose detection in an aqueous solution. Multiple wavelength measurements have the potential to compensate for errors associated with inter- and intra-individual differences in blood and tissue components. In this study, the transmission measurements of a custom built optical sensor are examined using 18 different wavelengths between 410 and 940 nm. Results show a high correlation value (0.98) between glucose concentration and transmission intensity for four wavelengths (485, 645, 860 and 940 nm). Five machine learning methods are investigated for glucose predictions. When regression methods are used, 9% of glucose predictions fall outside the correct range (normal, hypoglycemic or hyperglycemic). The prediction accuracy is improved by applying classification methods on sets of data arranged into 21 classes. Data within each class corresponds to a discrete 10 mg/dL glucose range. Classification based models outperform regression, and among them, the support vector machine is the most successful with F1-score of 99%. Additionally, Clarke error grid shows that 99.75% of glucose readings fall within the clinically acceptable zones. This is an important step towards critical diagnosis during an emergency patient situation.

Single-wavelength water muted photoacoustic system for detecting physiological concentrations of endogenous molecules

Based on the breakthrough technology of water muting on photoacoustic spectroscopy, a single wavelength photoacoustic system in the short-wavelength-infrared (SWIR) region was developed to sense the endogenous molecules (e.g. glucose, lactate, triglyceride, and serum albumin found in blood and interstitial fluid) in aqueous media. The system implemented a robust photoacoustic resonant cell that can significantly enhance the signal-to-noise ratio of the acoustic waves. The sensitivity of the system was explored, and the experimental results exhibit a precision detection of physiological concentrations of biomolecules by combining the techniques of water muting and photoacoustic resonant amplification in a portable and low-cost single wavelength laser system.

Non-Invasive Blood Glucose Monitoring Technology: A Review

In recent years, with the rise of global diabetes, a growing number of subjects are suffering from pain and infections caused by the invasive nature of mainstream commercial glucose meters. Non-invasive blood glucose monitoring technology has become an international research topic and a new method which could bring relief to a vast number of patients. This paper reviews the research progress and major challenges of non-invasive blood glucose detection technology in recent years, and divides it into three categories: optics, microwave and electrochemistry, based on the detection principle. The technology covers medical, materials, optics, electromagnetic wave, chemistry, biology, computational science and other related fields. The advantages and limitations of non-invasive and invasive technologies as well as electrochemistry and optics in non-invasives are compared horizontally in this paper. In addition, the current research achievements and limitations of non-invasive electrochemical glucose sensing systems in continuous monitoring, point-of-care and clinical settings are highlighted, so as to discuss the development tendency in future research. With the rapid development of wearable technology and transdermal biosensors, non-invasive blood glucose monitoring will become more efficient, affordable, robust, and more competitive on the market.

Non-Invasive Real-Time Monitoring of Glucose Level Using Novel Microwave Biosensor Based on Triple-Pole CSRR

Planar microwave sensors are considered an attractive choice to noninvasively probe the dielectric attributes of biological tissues due to their low cost, simple fabrication, miniature scale, and minimum risk to human health. This paper develops and measures a novel microwave biosensor for non-invasive real-time monitoring of glucose level. The design comprises a rectangular plexiglass channel integrated on a triple-pole complementary split ring resonator (TP-CSRR). The proposed sensor operates in the centimeter-wave range 1-6 GHz and is manufactured using PCB on top of an FR4 dielectric substrate. The sensor elements are excited via a coupled microstrip transmission-line etched on the bottom side of the substrate. The integrated CSRR-based sensor is used as a near-field probe to non-invasively monitor the glucose level changes in the blood mimicking solutions of clinically relevant concentrations to Type-2 normal diabetes (70-120 mg/dL), by recording the frequency response of the harmonic reflection and transmission resonances. This indicates the sensor's capability of detecting small variations in the dielectric properties of the blood samples that are responsive to the electromagnetic fields. The proposed sensor is verified through practical measurements of the fabricated design. Experimental results obtained using a Vector Network Analyzer (VNA) demonstrate a sensitivity performance of about 6.2 dB/(mg/ml) for the developed triple-pole sensor that significantly outperforms the conventional single-pole and other proposed sensors in the literature in terms of the resonance amplitude resolution.

Biosensors Based on Mechanical and Electrical Detection Techniques

Biosensors are powerful analytical tools for biology and biomedicine, with applications ranging from drug discovery to medical diagnostics, food safety, and agricultural and environmental monitoring. Typically, biological recognition receptors, such as enzymes, antibodies, and nucleic acids, are immobilized on a surface, and used to interact with one or more specific analytes to produce a physical or chemical change, which can be captured and converted to an optical or electrical signal by a transducer. However, many existing biosensing methods rely on chemical, electrochemical and optical methods of identification and detection of specific targets, and are often: complex, expensive, time consuming, suffer from a lack of portability, or may require centralised testing by qualified personnel. Given the general dependence of most optical and electrochemical techniques on labelling molecules, this review will instead focus on mechanical and electrical detection techniques that can provide information on a broad range of species without the requirement of labelling. These techniques are often able to provide data in real time, with good temporal sensitivity. This review will cover the advances in the development of mechanical and electrical biosensors, highlighting the challenges and opportunities therein.

Low-cost portable microwave sensor for non-invasive monitoring of blood glucose level: novel design utilizing a four-cell CSRR hexagonal configuration

This article presents a novel design of portable planar microwave sensor for fast, accurate, and non-invasive monitoring of the blood glucose level as an effective technique for diabetes control and prevention. The proposed sensor design incorporates four cells of hexagonal-shaped complementary split ring resonators (CSRRs), arranged in a honey-cell configuration, and fabricated on a thin sheet of an FR4 dielectric substrate.The CSRR sensing elements are coupled via a planar microstrip-line to a radar board operating in the ISM band 2.4-2.5 GHz. The integrated sensor shows an impressive detection capability and a remarkable sensitivity of blood glucose levels (BGLs). The superior detection capability is attributed to the enhanced design of the CSRR sensing elements that expose the glucose samples to an intense interaction with the electromagnetic fields highly concentrated around the sensing region at the induced resonances. This feature enables the developed sensor to detect extremely delicate variations in the electromagnetic properties that characterize the varying-level glucose samples. The desired performance of the fabricated sensor is practically validated through in-vitro measurements using a convenient setup of Vector Network Analyzer (VNA) that records notable traces of frequency-shift responses when the sensor is loaded with samples of 70-120 mg/dL glucose concentrations. This is also demonstrated in the radar-driven prototype where the raw data collected at the radar receiving channel shows obvious patterns that reflect glucose-level variations. Furthermore, the differences in the sensor responses for tested glucose samples are quantified by applying the Principal Component Analysis (PCA) machine learning algorithm. The proposed sensor, beside its impressive detection capability of the diabetes-spectrum glucose levels, has several other favorable attributes including compact size, simple fabrication, affordable cost, non-ionizing nature, and minimum health risk or impact. Such attractive features promote the proposed sensor as a possible candidate for non-invasive glucose levels monitoring for diabetes as evidenced by the preliminary results from a proof-of-concept in-vivo experiment of tracking an individual's BGL by placing his fingertip onto the sensor. The presented system is a developmental platform towards radar-driven wearable continuous BGL monitors.

Non-invasive continuous-time glucose monitoring system using a chipless printable sensor based on split ring microwave resonators

This paper reports a highly sensitive, non-invasive sensor for real-time glucose monitoring from interstitial fluid. The structure is comprised of a chip-less tag sensor which may be taped over the patient’s skin and a reader, that can be embedded in a smartwatch. The tag sensor is energized through the established electromagnetic coupling between the tag and the reader and its frequency response is reflected on the spectrum of the reader in the same manner. The tag sensor consumes zero power as there is no requirement for any active readout or communication circuitry on the tag side. When measuring changes in glucose concentrations within saline replicating interstitial fluid, the sensor was able to detect glucose with an accuracy of ~ 1 mM/l over a physiological range of glucose concentrations with 38 kHz of the resonance frequency shift. This high sensitivity is attained as a result of the proposed new design and extended field concentration on the tag. The impact of some of the possible interferences on the response of the sensor’s performance was also investigated. Variations in electrolyte concentrations within the test samples have a negligible effect on the response of the sensor unless these variations are supra-physiologically large.

Modelling of open photoacoustic resonators

Photoacoustic spectroscopy employs acoustic resonators for signal amplification. Resonators are usually closed, however, in some applications, open resonators are preferred. The opening deteriorates the photoacoustic signal hence reducing the sensitivity of the photoacoustic measurement. We present two new approaches for simulating the photoacoustic signal in open resonators using finite element modelling. The approaches are based on the amplitude mode expansion model and the viscothermal model with the opening modelled using perfectly matched layers and the boundary element method respectively. Additionally, the performance of the viscothermal model using perfectly matched layers for simulating open resonators is extended to the ultrasound region. The simulation results are verified by comparing them to photoacoustic measurements. The approaches provide an accurate basis for designing and optimizing open resonators with high sensitivity.