The increasing number of diabetic patients worldwide, especially in developed countries, requires an urgent need for better and painless alternatives to monitor blood glucose. The advantages of millimeter-wave sensors in the frequency range of 30-300 GHz in non-invasive monitoring include greater sensitivity to biological tissues and penetration of the skin without invasive procedures. This paper discusses the feasibility of using mmWave sensors for continuous glucose monitoring, based on variations in dielectric properties determined by blood glucose levels. This study focused on a miniaturized transceiver operating at 61 GHz based on FMCW radar; the usage scenario was within the frequency range of 60 to 64 GHz. The system adopts a digital signal processing-based approach to guarantee precise and reliable glucose measurements. The dimensions of the miniaturized transceiver are 76 × 56 × 27.6 mm, with excellent performance in detecting blood glucose levels from 25 mg/dL to 400 mg/dL. The R-squared value is more than 95%, indicating the high accuracy and reliability of the device in tracking glucose trends. This novel, non-invasive glucose monitoring technique holds great promise for real-time and accurate blood glucose detection, which would be more convenient and patient-friendly in diabetes management. Such a development opens a new door to an improved quality of life for diabetic patients and a potential reduction in complications associated with conventional invasive methods of monitoring.

Patients diagnosed with glioma have a 5-year survival rate of less than 5% [1]. Due to the difficulty distinguishing between healthy and tumour tissue, recurrence of glioma is likely to occur after surgery [2]. An area of interest in improving tissue distinguishment during surgery is fluorescence-guided surgery. One implementation utilises 5 aminolaevulinic acid (5-ALA) [3], which causes a build-up of Protoporphyrin IX (PpIX) in tumour tissue, caused by different metabolic processes between tumour tissue (glycolysis) and brain tissue (haem) [4]. PpIX fluoresces pink (635 nm peak) under blue light excitation (highest absorption at 405 nm) [5]. This fluorescence is difficult to see with the naked eye in lower concentrations of PpIX (found in edge cases of glioma). There have been many attempts to detect this fluorescence, though most devices are not suitable for miniaturization or for in-surgery use [6] [7]. To tackle this issue, a system for quantitatively measuring these fluorescence spectra has been developed. The system houses a miniature CMOS multispectral sensor alongside micro-LEDs to provide excitation and recording in a small footprint (3 mm x 3mm), which encourages further developments to integrate the system into existing surgical tools. The photosensor was benchmarked using a gelatine model mixed with ink to provide fluorescence close to the desired peaks (515 nm, 625 nm). The system mapped a fluorescence distribution area of 16 mm x 16 mm, with results being compared to a spectrometer's performance. The system achieved a high correlation with the spectrometer (R²>0.98), confirming its accuracy and suitability for real-time detection. Integrating this system into existing surgical tools can help in surgical detection of glioma tissue, increasing the glioma tissue removed, reducing the chance of recurrence, and increasing the survival rate of patients.

References

[1] A. F. Tamimi and M. Juweid, "Epidemiology and outcome of glioblastoma," Exon Publications, pp. 143-153, 2017, doi: http://dx​.doi.org/10​.15586/codon.glioblastoma.2017.ch8.

[2] D. Orringer et al., "Extent of resection in patients with glioblastoma: limiting factors, perception of resectability, and effect on survival," Journal of neurosurgery, vol. 117, no. 5, pp. 851-859, 2012, doi: https://doi.org/10.3171/2012.8.JNS12234.

[3] W. Stummer, H. Stepp, G. Möller, A. Ehrhardt, M. Leonhard, and H. Reulen, "Technical principles for protoporphyrin-IX-fluorescence guided microsurgical resection of malignant glioma tissue," Acta neurochirurgica, vol. 140, pp. 995-1000, 1998, doi: https://doi.org/10.1007/s007010050206.

[4] M. J. Colditz, K. Van Leyen, and R. L. Jeffree, "Aminolevulinic acid (ALA)–protoporphyrin IX fluorescence guided tumour resection. Part 2: Theoretical, biochemical and practical aspects," Journal of Clinical Neuroscience, vol. 19, no. 12, pp. 1611-1616, 2012, doi: https://doi.org/10.1016/j.jocn.2012.03.013.

[5] C. Von Dobbeler, L. Schmitz, K. Dicke, R. Szeimies, and T. Dirschka, "PDT with PPIX absorption peaks adjusted wavelengths: Safety and efficacy of a new irradiation procedure for actinic keratoses on the head," Photodiagnosis and Photodynamic Therapy, vol. 27, pp. 198-202, 2019, doi: https://doi.org/10.1016/j.pdpdt.2019.05.015.

[6] A. Gautheron et al., "Robust estimation of 5-ALA-induced PpIX contributions in multiple-wavelength excitation fluorescence spectroscopy to improve intraoperative glioma detection: application on clinical data," in Clinical Biophotonics III, 2024, vol. 13009: SPIE, pp. 31-35, doi: https://doi.org/10.1117/12.3022093.

[7] D. Black et al., "Towards machine learning-based quantitative hyperspectral image guidance for brain tumor resection," Communications Medicine, vol. 4, no. 1, p. 131, 2024, doi: https://doi.org/10.1038/s43856-024-00562-3.

Measuring bilirubin levels in blood or serum is a critical process for evaluating liver function and monitoring the effectiveness of various treatments for liver-related conditions. Abnormal bilirubin levels can indicate liver dysfunction, bile duct obstruction, or hemolytic disorders, making accurate measurement essential for timely diagnosis and intervention. This study focuses on assessing the clinical performance of a newly developed point-of-care (PoC) device specifically designed for rapid and reliable bilirubin measurement in serum samples. The PoC device integrates advanced technology, including a compact optoelectronic sensing module, which provides high sensitivity and precision, and a microfluidic test cartridge, enabling efficient sample handling and analysis.

To validate the performance of the PoC device, serum bilirubin levels were measured in 20 patient samples, covering a wide concentration range from 2 µmol/L to 480 µmol/L. These measurements were compared with those obtained using the standard laboratory method, which serves as the gold standard in clinical diagnostics. Statistical methods, including Bland–Altman analysis and Passing–Bablok regression, were employed to evaluate the agreement and correlation between the PoC device and standard laboratory results. Additionally, the diagnostic capability of the PoC device to classify samples based on clinically relevant bilirubin thresholds—specifically 200 µmol/L, 300 µmol/L, and 450 µmol/L—was assessed using receiver operating characteristic (ROC) analysis.

The results demonstrated that the PoC device produced measurements with a mean difference of -5.6 µmol/L compared to the standard laboratory method. The 95% confidence interval for this difference ranged from -45.1 µmol/L to 33.9 µmol/L, indicating acceptable limits of agreement for clinical use. Furthermore, the coefficient of determination (R²) between the two methods was 0.986, reflecting a strong correlation. The PoC device also exhibited robust diagnostic performance, achieving over 90% sensitivity and 97% specificity for correctly classifying bilirubin levels within the defined clinical thresholds. These findings highlight the potential of the PoC device as a reliable alternative to traditional laboratory methods, particularly in settings where rapid and on-site testing is required.

In conclusion, this study demonstrates that the proposed PoC device is capable of accurately measuring serum bilirubin levels with clinically acceptable precision and reliability. Its compact design, integration of advanced technologies, and high diagnostic accuracy make it a valuable tool for point-of-care applications in various healthcare settings. The device’s ability to provide quick and accurate results has the potential to improve patient outcomes by enabling timely clinical decision-making, especially in resource-limited environments or emergency care scenarios.

Introduction
Understanding the impact of hypogravity on human gait characteristics is crucial for upcoming space exploration missions. Ground reaction force (GRF) and joint kinematics are critical gait parameters for assessing human locomotion. Traditional methods, such as force plates and motion capture systems, have been widely used to study GRF and joint kinematics under different walking speeds and inclines in normal Earth gravity. However, these methods are often complex, time-intensive, and require elaborate setups. While wearable sensing systems offer a simpler method and effectively track human walking motion, their application to analyzing human kinetics and kinematics in hypogravity environments has not been fully explored. To address these gaps, we propose a wearable motion sensing system that integrates a custom-designed force insole, tailored to different gravity levels to measure GRF, and inertial measurement units (IMUs) to track lower limb joint kinematics. This system was employed to evaluate human gait characteristics and kinematics under simulated hypogravity conditions, combining GRF and joint kinematics for a comprehensive analysis.

Methods
We first developed a mechanical suspension platform capable of simulating adjustable hypogravity conditions for level walking experiments. Our wearable motion sensing system comprises four IMUs and a custom force insole, enabling the real-time monitoring of lower limb joint acceleration, angular velocity, Euler angles, and GRF during hypogravity walking experiments.

Results and Conclusion
The wearable motion sensing system successfully monitored lower limb joint kinematics and GRF simultaneously, revealing new kinematic characteristics during hypogravity walking. The custom force insole accurately captured GRF trends under these conditions. This system provides a robust tool for investigating gait characteristics and human kinematics in hypogravity environments, offering insights into optimizing mobility strategies, enhancing wearable device design, and understanding biomechanical adaptations under such conditions.

Metabolic diseases are increasing in relevance both in health and the economy in most countries. In this direction, if gold-standard technologies are to be based on blood analysis, non-invasive glucose monitoring is a relevant and great challenge that has not yet been fully resolved. This paper provides an overview of the patent landscape related to wearable biosensors for the monitoring of glucose levels in sweat.

Sweat represents a more suitable medium for the non-invasive sensing and monitoring of glucose than other bodily fluids, such as saliva, tears, or urine. However, the measurement of glucose levels requires the use of highly precise and sensitive sensors, given the low glucose concentration in sweat.

Patents’ data were retrieved from the Espacenet database (www.espacenet.com), provided by the European Patent Office and freely accessible. The search strategy was based on 3 main keywords: “wearable" AND "sweat" AND "glucose”. A set of single sub-keywords allowed for further data retrieval and clustering.

A total of 115 records were collected from Espacenet. After excluding some records that were identified as duplicates or related to applications rather than wearable devices, 95 records were included in the review.

China (63) is the country with the highest number of filings, followed by the USA (28) and Europe (21). The first application was filed in 2006; however, it was not until 2014 that an upward trend in filings became evident, with notable peaks in 2017 and 2021.

A total of 41.5% of the applications are currently pending, while only 35.1% have been granted patents.

The majority of claimed electrochemical sensors are enzymatic sensors.

Graphene represents the most prevalent carbon material utilized in the electrode, followed by rGO and carbon nanotubes. The employment of MXenes and MOF is comparatively limited.

The power supply unit may include a solar cell, a fuel cell, or a lithium-ion battery, but a small-sized lithium battery is preferably used.

This analysis aims to identify promising technologies and related IP for the non-invasive assessment of glucose in wearable systems: continuous monitoring, reliability, and interaction with infusion pumps are just the start.

In the field of medicine and wearable health devices, the need to monitoring cardiac activity continuously represents a challenge. Due to the random nature of many pathologies affecting the cardiac system, it is often necessary to continuously monitor cardiac activity. This means that portable ECG devices have to cope with movement noise, which is normally not present during an ambulatory measurement.

The aim of this study is to present a 12-lead ECG portable system based on TI ADS1298 and LSM6DSV16X from STMicroelectronics. The proposed device is capable of real-time ECG acquisition, managing to solve the problem of movement noise. A threshold mechanism was therefore implemented to allow the acquisition of the ECG signal based on the signal read by the accelerometer.

A threshold mechanism was therefore implemented to allow the acquisition of the ECG signal based on the signal read by the accelerometer. As a result, patients are able to carry out their daily activities while remaining continuously monitored.

The entire system has been extensively tested, and this article demonstrates its function. It was also demonstrated that the proposed layout does not degrade the performance recorded by the chip used.

We are currently developing a wearable board characterized by reduced weight, better portability, wireless connectivity, and integration of bioimpedance sensors.

The increasing prevalence of mental health disorders, such as chronic stress and anxiety, emphasizes the critical need for advanced monitoring systems that can provide actionable insights into psychological well-being. This study presents a novel ring-based biosensor platform that integrates multi-modal data analysis with Tiny Machine Learning (TinyML) for real-time stress and mental health assessment. The proposed system analyzes key physiological and biochemical markers of stress, including electrodermal activity through galvanic skin response (GSR) sensors, heart rate variability (HRV) for autonomic nervous system analysis, and cortisol levels as a primary stress biomarker. TinyML models embedded in the ring enable efficient on-device processing of biosensor data, identifying trends and patterns in stress markers while minimizing power consumption. This approach allows the system to deliver timely alerts for potential stress or anxiety episodes and provide personalized interventions, such as guided relaxation exercises. The localized computation ensures enhanced data privacy, low latency, and reduced reliance on external cloud services. Designed to be lightweight and ergonomic, the ring is optimized for continuous wear, making it suitable for long-term monitoring and for the early detection of stress-related conditions. Validation of the platform is conducted using established TinyML performance metrics, including sensitivity, specificity, latency, and memory footprint, ensuring reliable and efficient operation in a resource-constrained wearable device. This work demonstrates the potential of combining wearable biosensors with embedded machine learning to advance personalized mental health management and stress mitigation.

The real-time monitoring of sweat lactate provides valuable physiological insights for assessing exercise outcomes and athletic performance. Conventional lactate detection methods often lack sensitivity, portability, and user-friendliness for in-body applications. To address these limitations, electrochemical biosensing has emerged as a leading approach, enabling non-invasive, real-time analysis. Wearable bioelectronic devices integrate lactate-specific enzymes with electrochemical transducers, offering practical solutions for continuous monitoring. In this study, a wearable lactate electrochemical biosensor was developed using custom screen-printed electrodes modified with a bio-hybrid probe comprising Prussian blue, carbon black, and lactate oxidase. After optimizing key parameters, the biosensor demonstrated a detection limit of 0.06 mM and a linear range up to 20 mM. A filter paper strip was incorporated to enhance sweat collection and serve as the sample chamber, achieving 6% repeatability and efficient sweat handling. The system was validated using three sweat samples, showing strong correlation (96–101%) with LC-MS/MS, a standard laboratory method. The biosensor was integrated into a 3D-printed thermoplastic polyurethane (TPU) armband, designed for efficient sweat collection and transport, combining lightweight durability with a customizable, ergonomic design suitable for dynamic activities. This low-cost, wearable system represents a significant step forward in non-invasive, continuous, and personalized health monitoring, providing a practical tool for tracking physiological parameters in real time.

Patient compliance and medical adherence are critical factors in achieving optimal treatment outcomes, reducing healthcare costs, and enhancing patients' quality of life. However, non-compliance and poor adherence remain persistent challenges, often stemming from factors such as complex treatment regimens, lack of patient education, financial barriers, and psychological resistance. These issues can compromise therapeutic efficacy, increase hospitalization rates, and burden healthcare systems. In recent years, technological advancements have paved the way for innovative solutions to address these challenges. Mobile health (mHealth) applications, wearable devices, artificial intelligence (AI)-driven interventions, telemedicine, and blockchain technology have emerged as powerful tools to improve medication adherence and patient engagement. These technologies offer personalized reminders, real-time health monitoring, predictive analytics, and secure data sharing, fostering better communication between patients and healthcare providers. AI algorithms can predict adherence risks, while blockchain ensures data transparency and security. Additionally, gamification strategies and virtual coaching have shown promise in motivating patients to adhere to treatment plans. This review comprehensively explores these cutting-edge technological approaches, analyzing their impact on bridging the adherence gap and revolutionizing patient care. By leveraging these advancements in healthcare system, healthcare systems can create more patient-centric models, ultimately leading to improved health outcomes and sustainable healthcare practices.

Abstract

Introduction: In recent times, biosensors have become advanced tools in medical diagnostics, utilizing artificial intelligence (AI) to detect specific biological disorders promptly. AI in the form of OpenPose models with wearable biosensors improves patients' daily routines and physiological changes in Alzheimer patient diagnostics. AI-based patient routine analysis covers complex patterns and offers fast processing with higher accuracy for physiological changes in Alzheimer patients. This model advances the detection of health condition changes, such as understanding patient routines, suggesting disease progression, and improving healthcare outcomes.

Methods: Modern healthcare technology has evolved, leading to the creation of a wearable biosensor that monitors human organs for real-time data analysis. It measures vital signs, physical activity, and sleep patterns to analyse physiological changes in patients. Wearable biosensors are cost-effective devices for detecting biomarkers associated with behavioral and cognitive changes in patients. This model allows caretakers of patients and healthcare professionals to track patients’ health and activities in real time, enabling early activity changes in Alzheimer patient diagnostics.

Results: Utilizing the full potential of AI models like OpenPose, trained on biomarker datasets, this wearable biosensor effectively handles diverse datasets, achieving a precision of 93.27% in Alzheimer patient activity analysis. This study successfully found changes in daily activity, like irregular sleep patterns, abnormal vital signs, and little physical activity. These systems also improve the accuracy of cognitive decline by allowing real-time monitoring of medical component response, which makes them more useful in healthcare diagnostics.

Conclusion: The integration of biosensors with OpenPose is transforming Alzheimer patient diagnostics, offering more accurate and effective solutions. This wearable biosensor-based AI model provides real-time health monitoring, personalized medical care, and early activity change detection and supports further medical component development. In the future, this biosensor-based AI will play an important role in improving global healthcare facilities and patient monitoring, leading to efficient outcomes.