Non-invasive Sensor Research Articles

Recycled cellulose/PVA/CMC-GO/NQS hydrogel as a noninvasive sarcosine sensor for prostate cancer screening

Recycled cellulose/PVA/CMC-GO/NQS hydrogel as a noninvasive sarcosine sensor for prostate cancer screening

Using Physiological Biomarkers to Optimize Management of TBI in Austere Environments.

Multimodal monitoring is the use of data from multiple physiological sensors combined in a way to provide individualized patient management. It is becoming commonplace in the civilian care of traumatic brain-injured patients. We hypothesized we could bring the technology to the battlefield using a noninvasive sensor suite and an artificial intelligence-based patient management guidance system. Working with military medical personnel, we gathered requirements for a hand-held system that would adapt to the rapidly evolving field of neurocritical care. To select the optimal sensors, we developed a method to evaluate both the value of the sensor's measurement in managing brain injury and the burden to deploy that sensor in the battlefield. We called this the Value-Burden Analysis which resulted in a score weighted by the Role of Care. The Value was assessed using 7 criteria, 1 of which was the clinical value as assessed by a consensus of clinicians. The Burden was assessed using 16 factors such as size, weight, and ease of use. We evaluated and scored 17 sensors to test the assessment methodology. In addition, we developed a design for the guidance system, built a prototype, and tested the feasibility. The resulting architecture of the system was modular, requiring the development of an interoperable description of each component including sensors, guideline steps, medications, analytics, resources, and the context of care. A Knowledge Base was created to describe the interactions of the modules. A prototype test set-up demonstrated the feasibility of the system in that simulated physiological inputs would mimic the guidance provided by the current Clinical Practice Guidelines for Traumatic Brain Injury in Prolonged Care (CPG ID:63). The Value-Burden analysis yielded a ranking of sensors as well as sensor metadata useful in the Knowledge Base. We developed a design and tested the feasibility of a system that would allow the use of physiological biomarkers as a management tool in forward care. A key feature is the modular design that allows the system to adapt to changes in sensors, resources, and context as well as to updates in guidelines as they are developed. Continued work consists of further validation of the concept with simulated scenarios.

Relative Blood Volume Monitoring during Continuous Renal Replacement Therapy: A Prospective Observational Study.

Hematocrit monitoring during continuous renal replacement therapy (CRRT) allows the continuous estimation of relative blood volume (RBV). This may enable early detection of intravascular volume depletion prior to clinical sequelae. We aimed to investigate the feasibility of extended RBV monitoring and its epidemiology during usual CRRT management by clinicians unaware of RBV. Moreover, we studied the association between changes in RBV and net ultrafiltration (NUF) rates. In a cohort of adult intensive care unit patients receiving CRRT, we continuously monitored hematocrit and RBV using a pre-filter noninvasive optical sensor. We analyzed temporal changes in RBV and investigated the association between RBV change and NUF rates, using the classification of NUF rates into low, moderate, or high based on predefined cut-offs. We obtained >60,000 minute-by-minute measurements in >1,000 CRRT hours in 36 patients. The median RBV change was negative (decrease) in 69% of patients and the median peak change in RBV was -9.3% (interquartile range: -3.9% to -14.3%). Moreover, the median RBV decreased from baseline by >5% in 40.2% of measurements and by >10% in 20.6% of measurements. Finally, RBV decreased significantly more when patients received a high NUF rate (>1.75 mL/kg/h) compared to low or moderate NUF rates (5.32% vs. 1.93% or 1.97%, p < 0.001). Continuous hematocrit and RBV monitoring during CRRT was feasible. RBV decreased significantly during CRRT, and decreases were greater with higher NUF rates. RBV monitoring may help optimize NUF management and prevent the occurrence of intravascular volume depletion.

Non-Invasive Detection of Cytokines in Saliva Towards Asthma Phenotyping Using an Electrochemical Sensor

Introduction: Asthma is a complicated and heterogeneous condition of the lungs which is usually associated with the chronic inflammation of the airways. Traditionally, classification of asthma has been based on clinical attributes such as the symptoms observed and response to treatment. However, with the growing understanding of the heterogeneity of asthma as a spectrum with varying underlying causes, asthma phenotyping has been on the horizon. Cytokines are the chemical messengers that play a crucial role in the immune system. They mediate communication between the cells in the immune system and as such have a direct effect on the inflammation process. The role of some cytokines has been established in the pathogenesis of asthma. Due to the involvement of cytokines in the modulation of asthma, they can be excellent target for asthma phenotyping. This is also helpful because identification and quantification of these inflammatory mediators can allow for more precise medicine for more targeted treatment. To aid the identification and quantification of cytokines in the phenotyping of asthma, we have created a customizable (currently for IL-8 and IL-10) multi-marker electrochemical platform for the detection of cytokines non-invasively in saliva. The developed electrochemical platform can detect cytokines rapidly and non-invasively within reported physiological ranges of the given cytokines. Evaluating the presence and quantity of the cytokines will be useful in the phenotyping and treatment plans adopted by physicians.Materials and Methods: The electrochemical sensor consists of 6 multiplexed gold electrodes on a PCB substrate. IL-8 and IL-10 antibodies were immobilized onto the sensor surface using a gold-thiol linker chemistry. Sensor response was evaluated with non- faradaic Electrochemical Impedance Spectroscopy (nf-EIS). Pooled human saliva was spiked with increasing doses of IL-8 and IL-10 in their respective physiological ranges to develop a dose response curve. Spike and recovery experiments were conducted to validate the efficacy of dose response curve.Results and Discussion: Sensor response to both IL-8 and IL-10 spiked doses in pooled saliva showed a dose-dependent response to increasing concentrations of IL-8 and IL-10 respectively as illustrated by the Nyquist plots in Fig.1. The change in impedance with increasing doses of IL-8 and IL-10 in pooled human saliva lends to credence to the establishment of the presence and quantification of cytokines in saliva that can be used by clinicians and scientists alike in the further development of personalized medicine for severe asthmatics unresponsive to traditional asthmatic treatments.Conclusion: This study presents a noninvasive electrochemical sensor for rapid and early detection of cytokines in pooled human saliva towards asthma phenotyping. The sensor exhibits a dose dependent response to IL-8 and IL-10, markers implicated in pathogenesis of asthma. Sensor capability was demonstrated with spike and recovery experiments illustrating the ability of the developed electrochemical sensor to detect and quantify cytokines in human saliva. Figure 1

An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements.

Stress has various impacts on the health of human beings. Recent success in wearable sensor development, combined with advancements in deep learning to automatically detect features from raw data, opens several interesting applications related to detecting emotional states. Being able to accurately detect stress-related emotional arousal in an acute setting can positively impact the imminent health status of humans, i.e., through avoiding dangerous locations in an urban traffic setting. This work proposes an explainable deep learning methodology for the automatic detection of stress in physiological sensor data, recorded through a non-invasive wearable sensor device, the Empatica E4 wristband. We propose a Long-Short Term-Memory (LSTM) network, extended through a Deep Generative Ensemble of conditional GANs (LSTM DGE), to deal with the low data regime of sparsely labeled sensor measurements. As explainability is often a main concern of deep learning models, we leverage Integrated Gradients (IG) to highlight the most essential features used by the model for prediction and to compare the results to state-of-the-art expert-based stress-detection methodologies in terms of precision, recall, and interpretability. The results show that our LSTM DGE outperforms the state-of-the-art algorithm by 3 percentage points in terms of recall, and 7.18 percentage points in terms of precision. More importantly, through the use of Integrated Gradients as a layer of explainability, we show that there is a strong overlap between model-derived stress features for electrodermal activity and existing literature, which current state-of-the-art stress detection systems in medical research and psychology are based on.

Frequency tunable non-invasive microwave blood glucose sensor based on negative impedance circuit

Abstract This article proposes a frequency adjustable non-invasive blood glucose microwave sensor based on negative impedance circuit. Firstly, this article establishes a skin-blood-skin human tissue model for the detection of non-invasive microwave sensors. Compared to traditional passive microwave blood glucose sensors, this design uses a negative impedance compensation circuit (NIC), greatly increasing microwave penetration and improving its detection sensitivity. In addition, this design achieves the goal of adjustable operating frequency by adding an additional dielectric layer at the detection end. This function is of great significance for microwave non-invasive blood glucose sensors. On the one hand, it can adjust to the optimal operating frequency band according to the characteristics of blood glucose changes; On the other hand, it is possible to measure changes in blood glucose concentration at multiple frequency points, thereby increasing the accuracy of detection. The relevant results show that adjusting the frequency band can reach 1–4 GHz, and the specific method of frequency adjustment is provided in the article; Meanwhile, four initial frequency points were selected in the article, at which the results show that the sensitivity reaches 1.356 × 10−3, 3.142 × 10−3, 2.071 × 10−3, 1.903 × 10−3. The above results demonstrate the superiority of our design, and blood glucose sensors based on this principle are expected to break through the limitations of current blood glucose sensors.

Application of Spatial Offset Raman Spectroscopy (SORS) and Machine Learning for Sugar Syrup Adulteration Detection in UK Honey.

Honey authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar adulteration in UK honeys. Through-container spatial offset Raman spectroscopy (SORS) was employed on 17 different types of natural honeys produced in the UK over a season. These samples were then spiked with rice and sugar beet syrups at the levels of 10%, 20%, 30%, and 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprints using different algorithms, namely PLS-DA, XGBoost, and Random Forest, with the aim to detect the level of adulteration per type of sugar syrup. The best-performing algorithm for classification was Random Forest, with only 1% of the pure honeys misclassified as adulterated and <3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, SORS spectra were collected from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica cinerea) produced in the UK and corresponding subsamples spiked with high fructose sugar cane syrup, and an exploratory data analysis with PCA and a classification with Random Forest were performed, both showing clear separation between the pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in the field with potential application at all stages of the supply chain.

Comparison of continuous temperature measurement methods in the intensive care unit: standard bladder catheter measurements versus non-invasive transcutaneous sensors.

The purpose of this study was to compare a wearable system for body core temperature measurement versus bladder and tympanic thermometers in an intensive care setting. The question was, if continuous non-invasive sensors in the intensive care unit represent an alternative to current standard methods of invasive continuous bladder temperature measurement methods?Between May and September 2023, a comparative investigation involving 112 patients was conducted in a 20-bed surgical intensive care unit to assess various temperature probes, including those placed in the tympanic tube, bladder, and skin. To achieve this, a wireless non-invasive sensor system provided by greenTEG AG, Switzerland, was affixed to different body locations (clavicular and lateral chest) of each catheterized patient (equipped with a temperature probe) admitted to the intensive care unit. Furthermore, tympanic temperatures were recorded at specified intervals. The measurement duration ranged from a minimum of six hours to a maximum of six days, resulting in the analysis of a total of 355 simultaneous temperature measurements.In this study, a wearable temperature measurement system attached to two different body sites revealed a consistent negative bias compared to bladder temperature. In addition, the measurements were particularly influenced by body constitution. The tested system in all patients showed a mean absolute error (MAE) of 0.45°C for the lateral chest and 0.50°C for the clavicular position. Tympanic measurements had a mean absolute error of 0.35°C. In patients with body mass index (BMI) ≥ 25 the MAE increased to 0.5°C for the lateral chest and 0.56°C for the clavicular position. In contrast, the tympanic measurement had a reduced MAE of 0.32°C, which is well below this threshold when compared to bladder measurements.In conclusion the investigated system did not meet the clinically relevant acceptance criteria and showed low precision in correctly identifying fever episodes compared to invasive temperature probes, however its main advantage lies in its continuity and non-invasiveness. This makes it a potential alternative to intermittent tympanic measurement devices. In this study we were able to show, that in at least one subset of patients, the non-invasive and continuous device demonstrated a precision comparable to tympanic measurements.The accuracy of all non-invasive methods was lower than in previous studies, suggesting that the use of bladder temperature as reference and user related variations may have introduced additional errors.

Non-Invasive Disposable 2D Ti3C2Tx based Enzyme Free Electrochemical Sweat Glucose Biosensor

Modern analytical chemistry, enhanced by advancements in materials science, is crucial for cost-effectively improving measurement techniques, and making analyses more accessible and user-friendly. Disposable screen-printed electrodes have emerged as promising tools, offering miniaturization, high reproducibility, high sensitivity, rapid detection, and inherent selectivity. This study highlights the development of advanced 2D Ti3C2Tx MXene-modified disposable screen-printed gold electrodes for non-invasive, enzyme-free glucose sensing. Ti3C2Tx was synthesized using direct and in-situ etchants to achieve controlled surface chemistry. Techniques such as XRD, UV–visible spectroscopy, SEM, and TEM were employed to characterize Ti3C2Tx synthesized with different etchants. The synthesized materials were then used as working electrodes to investigate their electrocatalytic performance, tailored for continuous glucose monitoring. Upon optimization, in-situ LiF and HCl etched Li-Ti3C2Tx@SPGE exhibited a high sensitivity of 569.70 µA mM−1cm−2, followed by HF-Ti3C2Tx@SPGE and N-Ti3C2Tx@SPGE with sensitivities of 249.62 and 297.78 µA mM−1cm−2, respectively. These sensors demonstrated low limits of detection (LOD) of 5 nM, 28 nM, and 19 nM within a linear range of 5 to 350 µM. Additionally, real sweat analysis was conducted by collecting natural sweat through fast walking and evaluating sensor performance after beverage consumption. Overall, Li-Ti3C2Tx@SPGE, without any additional composition or doping, shows potential as a superior commercial sweat sensor for routine glucose monitoring. This study underscores the innovative use of 2D materials in developing highly sensitive, non-invasive sweat glucose sensors that are both effective and accessible.

EGFET-Based Noninvasive Glucose Sensor Integrated with Enzyme-Mimicking Bi2O3 Microrods Derived from Bi-BTC Tested in Artificial Sweat

EGFET-Based Noninvasive Glucose Sensor Integrated with Enzyme-Mimicking Bi₂O₃ Microrods Derived from Bi-BTC Tested in Artificial Sweat

Design and development of a feedback system for automatic treadmill speed adaptation

Treadmills with automatic speed adjustment offer a unique advantage: they can mimic outdoor conditions for specific training or testing protocols. This versatility makes them highly applicable in both sports and rehabilitation settings. This study presents a novel framework based on a feedback control loop system, conceived to control a treadmill belt speed using a non-invasive and low-cost sensor (Microsoft Kinect) to detect the users’ position and avoid object obstruction issues. The speed of the treadmill belt is regulated according to the user’s speed, by means of a proportional-integrative-derivative (PID) controller and a parabolic gain function. By tuning the gain function parameters, the user can customize the response of the treadmill. Position data collected during exercise using the Microsoft Kinect sensor was compared with that collected with a stereophotogrammetric motion capture system, showing promising results in terms of accuracy in position assessment. The comparison highlighted a 0.9 degree of correlation between the two systems during the running and cross-country indoor skiing tests performed. In addition, upon considering the relationship between the differences and averages of the two measures, no systematic bias was identified. The system proved to be functional for running and cross-country skiing, and it can therefore re-create similar typical characteristics of outdoor environments (e.g., speed and slope) thanks to the non-invasive user’s position detection and the customizable gain function.

A simple physical model for simulation and design magneto-plethysmograph in application non-invasive hemoglobin measurement

The magneto-plethysmograph method is a combination of magnetic field and sensors used to detect changes in blood flow pulsation. However, to detect the magnetic properties of blood related to hemoglobin concentration, physical modeling and simulation are required. This approach involves designing simulations using magnetic field equations and magnetic susceptibility, where a permanent magnet is placed on the surface of blood vessels, and sensors based on giant magnetoresistance are placed at a distance r. The design originates from a simple approach involving the magnetization and detection of Fe atoms in hemoglobin. Parameters involved include the magnetic susceptibility of oxyhemoglobin and deoxyhemoglobin, with an external magnetic field exceeding 1 Tesla. From the physical modeling and simulation, graphs are obtained depicting the influence of hemoglobin concentration on the number of Fe atoms and its magnetization. This enables the design of non-invasive hemoglobin measurement sensor devices. The uniqueness of this simple physical model and simulation lies in its ability to produce specially designed device models for measuring hemoglobin concentration. This differs from other research focusing on blood flow pulse measurements; the results of this study provide new insights into the benefits of simple physics equations that can be developed for medical diagnostic research and device development.

Review on IoT-Driven Infusion Monitoring &amp; Management System

Effective intravenous (IV) infusion management and monitoring are essential in today's healthcare environment to guarantee patient safety and improve the standard of care. In order to overcome the drawbacks of conventional IV drip monitoring techniques, this study describes the creation of a contactless, Internet of Things-driven infusion monitoring and management system. With the use of cutting-edge sensor technology and Internet of Things connection, the suggested system combines real-time IV drip liquid level monitoring and control, delivering precise and ongoing updates. Healthcare practitioners may now monitor and analyse patient data remotely thanks to the gathered data's constant updating and transmission to the Think Speak cloud platform. With its combination of real- time data collecting, cloud-based analytics, and emergency alert features, the proposed contactless IoT-driven infusion monitoring and management system represents a substantial improvement in healthcare technology. Using the power of IoT and cloud computing, this invention seeks to enhance overall patient outcomes, optimize IV treatment administration, and decrease human monitoring efforts. To increase patient safety, the system has an emergency alarm mechanism based on GSM modules. This feature ensures that caregivers are notified promptly of any irregularities or critical IV drip levels, allowing for prompt treatment. By providing real-time signals, the device lessens the risks related to delayed reactions to IV fluid failure or depletion. Formulating a real-time model for IV drip liquid level monitoring is the main goal of the system. By employing non-invasive sensors to identify and record the drip rate and fluid levels, this gadget assures precise tracking without having physical contact with the IV apparatus. Keywords: Internet of Things, ESP32 microcontroller, Biomedical, GSM, Relay, LCD Display.

Embroidered Interdigitated Electrodes (IDTs) with Wireless Readout for Continuous Biomarker Monitoring.

Non-invasive continuous health monitoring has become feasible with the advancement of biosensors. While monitoring certain biomarkers such as heart rate or skin temperature are now at a certain maturity, monitoring molecular biomarkers is still challenging. Progress has been shown in sampling, measurement, and interpretation of data toward non-invasive molecular sensors that can be integrated into daily wearable items. Toward this goal, this paper explores the potential of embroidered interdigitated transducer (IDT)-based sensors for non-invasive, continuous monitoring of human biomarkers, particularly glucose levels, in human sweat. The study employs innovative embroidery techniques to create flexible fabric-based sensors with gold-coated IDTs. In controlled experiments, we have shown the variation of glucose concentration in water can be wirelessly detected by tracking the resonant frequency of the embroidered sensors. The current sensors operate at 1.8 GHz to 2 GHz and respond to the change in glucose concentration with a sensitivity of 0.17 MHz/(mg/dL). The embroidered IDT-based sensors with wireless sensing will be a new measurement modality for molecular wearable sensors. The establishment of a wireless sensing mechanism for embroidered IDT-based sensors will be followed by an investigation of sweat for molecular detection. This will require adding functionalities for sampling and interpretation of acquired data. We envisage the embroidered IDT-based sensors offer a unique approach for seamless integration into clothing, paving the way for personalised, continuous health data capture.

Developing a Swallow-State Monitoring System Using Nasal Airflow, Surface Electromyography, and Thyroid Cartilage Movement Detection.

The safe ingestion of food and water requires appropriate coordination between the respiratory and swallowing pathways. This coordination can be disrupted because of aging or various diseases, thereby resulting in swallowing disorders. No comparative research has been conducted on methods for effectively screening swallowing disorders in individuals and providing timely alerts to their caregivers. Therefore, the present study developed a monitoring and alert system for swallowing disorders by using three types of noninvasive sensors, namely those measuring nasal airflow, surface electromyography signals, and thyroid cartilage movement. Two groups of participants, one comprising healthy individuals (58 participants; mean age 49.4 years) and another consisting of individuals with a history of unilateral stroke (21 participants; mean age 54.4 years), were monitored when they swallowed five volumes of water. Through an analysis of the data from both groups, seven indicators of swallowing disorders were identified, and the proposed system characterized the individual's swallowing state as having a green (safe), yellow (unsafe), or red (highly unsafe) status on the basis of these indicators. The results indicated that the symptoms of swallowing disorders are detectable. Healthcare professionals can then use these data to conduct assessments, perform screening, and provide nutrient intake suggestions.

Skin carotenoid scores and metabolic syndrome in a general Japanese population: the Hisayama study

BackgroundHigher vegetable intake is being promoted as an initiative to prevent lifestyle-related diseases. Carotenoids are yellow or red pigment components and are widely present in vegetables. Since ingested carotenoids accumulate in the skin, skin carotenoid levels are a quantitative indicator of vegetable intake. Recently, noninvasive optical sensors for assessing skin carotenoid levels were developed. We here examined the association between skin carotenoid scores measured using optical sensors and the presence of metabolic syndrome.MethodsA total of 1618 individuals (604 men and 1014 women) aged ≥ 40 years (mean age 63.1 years) participated in the study. Skin carotenoid scores were determined using a noninvasive optical sensor based on multiple spatially resolved reflectance spectroscopy. Metabolic syndrome was defined based on the Joint Scientific Statement criteria developed by six international scientific societies. Multivariable-adjusted logistic regression models were used.ResultsThe prevalence of metabolic syndrome was 31.3% (n = 506). A remarkably strong association was found between higher skin carotenoid scores and lower prevalence of metabolic syndrome after adjusting for confounders. The multivariable-adjusted odds ratio for the presence of metabolic syndrome in individuals with the highest quartile of skin carotenoid scores was 0.39 (95% confidence interval, 0.28–0.55) compared to those with the lowest quartile.ConclusionsOur findings suggest that higher skin carotenoid scores measured by non-invasive optimal sensors are significantly associated with a lower likelihood of having metabolic syndrome in the general Japanese population.

A Gold Nanoparticle-Based Cortisol Aptasensor for Non-Invasive Detection of Fish Stress.

Cortisol is a key stress biomarker in humans and animals, including fishes. In aquafarming, stress monitoring using cortisol quantification can help to optimize aquaculture practices for welfare and productivity enhancement. However, most current methods for cortisol detection rely on invasive tissue sampling. In this work, we developed a gold nanoparticle (AuNP)-based cortisol sensor to address the demand of detecting picomolar ranges of cortisol from complex fish tank water matrices as a non-invasive alternative for more effective stress monitoring. We first identified a DNA aptamer with effective binding to cortisol and then conjugated the thiol-labelled aptamer to AuNPs together with a blocker molecule (CALNN) to form an Au-Apt-CALNN conjugate that is stable in fish tank water. The cortisol detection principle is based on magnesium chloride (MgCl2)-induced particle aggregation, where the cortisol-bound aptamer on the AuNPs folds into a tertiary structure and provides greater protection for Au-Apt-CALNN against MgCl2-induced aggregation due to steric stabilization. At an optimum MgCl2 concentration, the differential stability of particles with and without cortisol binding offers a limit of detection (LOD) of 100 pM for cortisol within a 35 min reaction. The aptasensor has been validated on recirculating aquaculture system (RAS) fish tank water samples by the HPLC method and was able to detect changes in water cortisol induced by two different stress paradigms. This on-site deployable and non-invasive sensor offers opportunities for more efficient and real-time fish stress monitoring for the optimization of aquaculture practices.

Non-Invasive Sensors Integration for NCDs with AIoT Based Telemedicine System.

Thailand's hospitals face overcrowding, particularly with non-communicable disease (NCD) patients, due to a doctor shortage and an aging population. Most literature showed implementation merely on web or mobile application to teleconsult with physicians. Instead, in this work, we developed and implemented a telemedicine health kiosk system embedded with non-invasive biosensors and time-series predictors to improve NCD indicators over an eight-month period. Two cohorts were randomly selected: a control group with usual care and a telemedicine-using group. The telemedicine-using group showed significant improvements in average fasting blood glucose (148 to 130 mg/dL) and systolic blood pressure (152 to 138 mmHg). Data mining with the Apriori algorithm revealed correlations between diseases, occupations, and environmental factors, informing public health policies. Communication between kiosks and servers used LoRa, 5G, and IEEE802.11, which are selected based on the distance and signal availability. The results support telemedicine kiosks as effective for NCD management, significantly improving key NCD indicators, average blood glucose, and blood pressure.

Wearable sensor systems measure differences in knee laxity between healthy and affected knees during dynamic exercise activities: Asystematic review.

Knee laxity can be experienced as knee instability which may lead to a limitation in the activity of patients. Current methods to determine knee instability are performed in a static setting, which does not always correlate with dynamic knee laxity during activities. Wearables might be able to measure knee laxity in a dynamic setting and could be of added value in the diagnosis and treatment of excessive knee laxity. Therefore, the aim of this systematic review is to provide an overview of the wearables that have been developed and their ability to measure knee laxity during dynamic activities. The PRISMA guidelines for systematic reviews were followed. A literature search was conducted in EMBASE, PubMed and Cochrane databases. Included studies assessed patients with knee instability using a non-invasive wearable sensor system during dynamic activity, with comparison to a reference system or healthy knees. Data extraction was performed by two authors via a predefined format. The risk of bias was assessed by The Dutch checklist for diagnostic tests. A total of 4734 articles were identified. Thirteen studies were included in the review. The studies showed a great variety of patients, sensor systems, reference tests, outcome measures and performed activities. Nine of the included studies were able to measure differences in patients with knee instability, all including a tri-axial accelerometer. Differences were not measurable in all parameters and activities in these studies. Wearables, including at least a tri-axial accelerometer, seem promising for measuring dynamic knee laxity in the anterior-posterior and mediolateral direction. At this stage, it remains unclear if the measured outcomes completely reflect the knee instability that patients experience in daily life. Level III.

A wearable non-enzymatic sensor for continuous monitoring of glucose in human sweat

To enhance personalized diabetes management, there is a critical need for non-invasive wearable electrochemical sensors made from flexible materials to enable continuous monitoring of sweat glucose levels. The main challenge lies in developing glucose sensors with superior electrochemical characteristics and high adaptability. Herein, we present a wearable sensor for non-enzymatic electrochemical glucose analysis. The sensor was synthesized using hydrothermal and one-pot preparation methods, incorporating gold nanoparticles (AuNPs) functionalized onto aminated multi-walled carbon nanotubes (AMWCNTs) as an efficient catalyst, and crosslinked with carboxylated styrene butadiene rubber (XSBR) and PEDOT:PSS. The sensors were then integrated onto screen-printed electrodes (SPEs) to create flexible glucose sensors (XSBR-PEDOT:PSS-AMWCNTs/AuNPs/SPE). Operating under neutral conditions, the sensor exhibits a linear range of 50 μmol/L to 600 μmol/L, with a limit of detection limit of 3.2 μmol/L (S/N = 3), enabling the detection of minute glucose concentrations. The flexible glucose sensor maintains functionality after 500 repetitions of bending at a 180° angle, without significant degradation in performance. Furthermore, the sensor exhibits exceptional stability, repeatability, and resistance to interference. Importantly, we successfully monitored changes in sweat glucose levels by applying screen-printed electrodes to human skin, with results consistent with normal physiological blood glucose fluctuations. This study details the fabrication of a wearable sensor characterized by ease of manufacture, remarkable flexibility, high sensitivity, and adaptability for non-invasive blood glucose monitoring through non-enzymatic electrochemical analysis. Thus, this streamlined fabrication process presents a novel approach for non-invasive, real-time blood glucose level monitoring.