Real-time Continuous Monitoring Research Articles

Mechanically programmable substrate enable highly stretchable solar cell arrays for self-powered electronic skin

Development of stretchable solar cells to accommodate large strain without sacrificing the power conversion efficiency is challenging. This study presents a novel approach to manufacture highly stretchable solar cell arrays, which can be integrated into wearable medical devices to operate under challenging environments. Overcoming the limitations of rigid metal wires, liquid metal wires are employed to connect single crystal silicon solar cells, ensuring both high conversion efficiency and exceptional stretchability (up to 100%). The manufacturing process introduces embedding electrospinning films into elastic silicone substrates to create a tight bond between rigid solar cells and stretchable substrates with programmable strain capabilities. Notably, a stretchable interconnect wire is efficiently fabricated using the semi-liquid metal template printing technique and demonstrates remarkable conductivity (up to 6 × 106 S/m). Individual structural unit of solar cell presents an impressive tensile rate of 200%, and endures up to 5000 cycles of tensile testing. The overall solar cell array exhibits outstanding adaptability to extensive bending and twisting, with a mere 0.1% short-circuit current reduction in its maximum stretched state. The stretchable photovoltaic device is successfully employed in creating self-powered electronic skin for continuous real-time monitoring of parameters, showcasing its valuable potential in wearable medical electronic devices and comprehensive sports health monitoring.

Radon Exposure Assessment in Occupational and Environmental Settings: An Overview of Instruments and Methods

Radon is a naturally occurring noble radioactive gas that poses significant health risks, particularly lung cancer, due to its colorless, odorless, and tasteless nature, which makes detection challenging without formal testing. It is found in soil, rock, and water, and it infiltrates indoor environments, necessitating regulatory standards and guidelines from organizations such as the Environmental Protection Agency, the World Health Organization, and the Occupational Health and Safety Agency to mitigate exposure. In this paper, we present various methods and instruments for radon assessment in occupational and environmental settings. Discussion on long- and short-term monitoring, including grab sampling, radon dosimetry, and continuous real-time monitoring, is provided. The comparative analysis of detection techniques—active versus passive—is highlighted from real-time data and long-term exposure assessment, including advances in sensor technology, data processing, and public awareness, to improve radon exposure evaluation techniques.

Imagining AI-driven decision making for managing farming in developing and emerging economies

Agriculture is the backbone of numerous developing and emerging economies, supporting millions of livelihoods and playing a crucial role. The transformative integration of AI-driven tools, big-data analytics, and advanced technologies, such as drones and satellites, can transform agricultural decision-making in these developing and emerging economies and globally. Acknowledging the intricate web of biophysical and socioeconomic factors that influence agricultural systems, we highlight the pressing need for innovative approaches. We explore the critical role of big-data analytics, Computational Fluid Dynamics (CFD), Machine Learning (ML), and remote sensing in transforming the sector, with a focus on enhancing efficiency, sustainability, and productivity. In this context, continuous real-time monitoring becomes essential, allowing farmers to manage their agricultural systems with precision. Implementing AI-driven tools and devices enables the collection and analysis of data in real time, leading to timely decisions that can significantly improve crop yields and resource management. This study discusses the impacts of big-data analytics on agriculture, including reduced pesticide use and improved crop yields. It presents concrete examples of AI applications in crop and animal farming, emphasizing precision irrigation, fertilizer management, animal health monitoring, and reproductive efficiency. We advocate for customizing these technologies to suit local contexts, which can lead to ensuring deep integration into developing and emerging economies' agricultural practices. In conclusion, the paper calls for collaborative efforts from governments, the private sector, and educational institutions to make these technologies accessible, affordable, and culturally relevant.

Single Voltammetric Sweep Calibration-Free Interrogation of Electrochemical Aptamer-Based Sensors Employing Continuous Square Wave Voltammetry.

Continuous square wave voltammetry (cSWV) is a technique that enables the continuous collection of current data (at 100 kHz) to maximize the information content obtainable from a single voltammetric sweep. This data collection procedure results in the generation of multiple voltammograms corresponding to different effective square wave frequencies. The application of cSWV brings significant benefits to electrochemical aptamer-based (E-AB) sensors. The E-AB sensor platform permits continuous real-time monitoring of small biological molecules. Traditionally, E-AB sensors report only on changes in analyte concentration rather than absolute quantification in matrices when basal concentrations are not known a priori. This is because they exhibit a voltammetric peak current even in the absence of a target. However, using a dual-frequency approach, calibration-free sensing can be performed effectively, eliminating the sensor-to-sensor variation by taking ratiometric current responses obtained at two different frequencies from two different voltammetric sweeps. In employing our approach, cSWV provides a great advantage over the conventionally used square wave voltammetry since the required voltammograms are collected with a single sweep, which improves the temporal resolution of the measurement when considering the current at multiple frequencies for improved accuracy and reduced surface interrogation. Moreover, we show here that using cSWV provides significantly improved concentration predictions. E-AB sensors sensitive to ATP and tobramycin were interrogated across a wide range of concentrations. With this approach, cSWV allowed us to estimate the target concentration, retaining up to an ±5% error of the expected concentration when tested in buffer and complex media.

Development of sensor system and data analytic framework for non-invasive blood glucose prediction

Periodic quantification of blood glucose levels is performed using painful, invasive methods. The proposed work presents the development of a noninvasive glucose-monitoring device with two sensors, i.e., finger and wrist bands. The sensor system was designed with a near-infrared (NIR) wavelength of 940 nm emitter and a 900–1700 nm detector. This study included 101 diabetic and non-diabetic volunteers. The obtained dataset was subjected to pre-processing, exploratory data analysis (EDA), data visualization, and integration methods. Ambiguities such as the effects of skin color, ambient light, and finger pressure on the sensor were overcome in the proposed ‘niGLUC-2.0v’. niGLUC-2.0v was validated with performance metrics where accuracy of 99.02%, mean absolute error (MAE) of 0.15, mean square error (MSE) of 0.22 for finger, and accuracy of 99.96%, MAE of 0.06, MSE of 0.006 for wrist prototype with ridge regression (RR) were achieved. Bland–Altman analysis was performed, where 98% of the data points were within ± 1.96 standard deviation (SD), 100% were under zone A of the Clarke Error Grid (CEG), and statistical analysis showed p < 0.05 on evaluated accuracy. Thus, niGLUC-2.0v is suitable in the medical and personal care fields for continuous real-time blood glucose monitoring.

Enhancing Parkinson’s Disease Prediction Using Deep Learning-Based Convolutional Neural Networks

Recently significant advances in the field of medical diagnostics can be seen. Nonetheless, diagnosing Parkinson's disease (PD) remains difficult, especially in terms of timeliness and precision. The inability of present technological methods to identify this complicated neurodegenerative disease emphasizes the urgent need for more research. PD is a complicated neurological condition that is becoming more commonplace worldwide. A timely and precise PD diagnosis is critical because it directly determines the quality of patient care and the success of treatment. However, our current diagnostic approaches, which primarily rely on clinical assessments, have substantial limits in terms of sensitivity and specificity. Thus, the need for a more reliable and objective diagnosing procedure is essential. Convolutional neural networks (CNNs) are employed in this study to investigate complex spiral and wave patterns and to overcome this significant challenge. Drawings from both PD patients and their healthy counterparts were included in the large dataset that was used to train the robust CNN model, which is a key component of the research technique. The training and intensive testing of the model on this dataset confirms its capacity to distinguish between PD instances and non-PD cases. The results of this investigation demonstrate how well the CNN model can predict Parkinson's disease (PD) based on spiral and wave patterns, with a 93% classification accuracy. The high sensitivity and specificity of the model are especially impressive because they significantly lower the chances of false positives and false negatives. The discussion that follows offers a considered evaluation of these findings, emphasising the model's potential for early diagnosis, its capacity to support professional judgments, and its crucial role in enhancing patient outcomes. This study highlights the great potential of deep learning methods for disease diagnosis, especially for PD. The effective integration of the CNN model into a React application for real-time prediction and continuous monitoring of patient performance has important implications for telemedicine and remote healthcare management. This breakthrough makes Parkinson's disease treatment more approachable and proactive.

Benzene: A critical review on measurement methodology, certified reference material, exposure limits with its impact on human health and mitigation strategies

Benzene is a carcinogenic pollutant with significant emission sources present in the atmosphere. The need for accurate and precise measurement of benzene in the atmosphere has become increasingly evident due to its toxicity and the adverse health effects associated with exposure to different concentrations. Certified reference material (CRM) is essential to establish the traceability of measurement results. The present review compiles the available national and international measurement methods, certified reference materials (CRMs) for benzene and the limit of benzene in fuel composition (v/v) worldwide. Overall, the review indicates the benzene level in the atmosphere and the resulting impacts on the environment and human health, which frequently exceed the exposure limits of different environment regulatory agencies. An extensive literature review was conducted to gather information on monitoring and analysis methods for benzene, revealing that the most preferred method, i.e. Gas Chromatography- Flame Ionization Detector and Mass Spectrometry, is neither cost-effective nor suitable for real-time continuous monitoring. By analysing existing literature and studies, this review will shed light on the understanding of the importance of benzene pollution monitoring in ambient air and its implications for public health. Additionally, it will reflect the mitigation strategies applied by regulators &amp; need for future revisions of air quality guidelines.

Noninvasive Microwave Sensor for Real-Time Continuous Dehydration Monitoring

Noninvasive Microwave Sensor for Real-Time Continuous Dehydration Monitoring

Computer vision-based real-time continuous monitoring of the pose for large-span bridge cable lifting structures

Given the highly intricate terrain and environmental conditions in the mountainous region, the existing measurement methods fail to ensure high precision and real-time long-distance measurements. The present study proposes a methodology for real-time and continuous monitoring of the bridge cable lifting structure pose over long distances. The main contributions include: (1) Designing a framework for a monocular vision-based 6 degrees of freedom (6-DOF) pose monitoring system that enables real-time measurement of bridge cable lifting structure displacements and angles during the construction of large-span bridges. (2) Developing an improved approach to address potential mislocalization of coordinate origin in the sequence ordering method used in traditional corner detection when the target proportion relative to the captured image area is small. The effectiveness of the proposed method is verified through the theoretical simulation tests, laboratory experiments, and monitoring of the actual use of the cable lifting structure during the construction of the XiNing River Extra large bridge.

Anomaly Detection in Heterogeneous Data Using Graph Convolutional Networks

Closed-circuit television (CCTV) surveillance systems are ubiquitous tools for ensuring public and private safety. However, manual monitoring of surveillance footage is arduous and time-consuming, often resulting in delayed identification and response to potential threats. In this paper, we present a novel real-time threat detection system tailored for CCTV surveillance, employing cutting-edge deep learning methodologies, particularly graph convolutional networks (GCNs), to detect and classify high-movement levels within video frames. By treating videos as segments and delineating between anomalous (potentially threatening) and normal (safe) segments, our system facilitates continuous real-time monitoring of surveillance footage, enabling the timely identification of potential threats such as abuse, burglaries, explosions, shootings, fighting, shoplifting, road accidents, arson, robbery, stealing, assault, and vandalism. To assess the efficacy of our system, we conducted extensive experiments on a large-scale dataset comprising CCTV footage, yielding promising results. Additionally, we introduce a novel, comprehensive dataset comprising 128 hours of real-world surveillance videos, encompassing various anomalies and normal activities. This dataset serves dual purposes: general anomaly detection, and the recognition of specific anomalous activities. Our experimental findings demonstrate the superior performance of our method in anomaly detection compared to state-of-the-art approaches. Furthermore, we provide an analysis of several recent deep learning baselines for anomalous activity recognition, revealing the challenging nature of our dataset and the potential it holds for future research endeavors. Through the integration of advanced deep learning techniques and comprehensive dataset creation, our proposed system stands to significantly enhance the efficiency and effectiveness of CCTV surveillance, facilitating faster response times and bolstering security for individuals in diverse environments.

IoT Based Air Quality Monitoring and Controlling System for Hospital

This project introduces an IoT based Air Quality Monitoring and Controlling System tailored for hospital environments. Utilizing a variety of sensors, including those for particulate matter, carbon dioxide, and volatile organic compounds, the system ensures continuous real-time monitoring of indoor air quality. NodeMCU facilitates wireless communication, enabling the transmission of data to a central server or cloud platform. Hospital staff can remotely monitor air quality conditions, receiving threshold alerts in the event of pollutant levels surpassing predefined limits. The system integrates a controlling mechanism, allowing for the activation of ventilation systems or air purifiers based on the collected data. Energy efficiency is prioritized through intelligent control of air quality devices. Historical data analysis enables administrators to identify patterns and improve overall air quality management. The system seamlessly integrates with existing hospital infrastructure and is designed for scalability, ensuring adaptability to evolving needs. Overall, this IoT-based solution offers a smart and automated approach to maintaining a healthy indoor environment in hospital settings.

Hemodialysis Efficiency Predictor in End-Stage Kidney Disease Using Real-Time Heart Rate Variability.

Autonomic dysfunction as a long-term complication may occur in end-stage kidney disease (ESKD) patients and can be diagnosed using heart rate variability (HRV) analyzed from electrocardiogram (ECG) recordings. There is limited data about HRV using real-time ECG to predict hemodialysis (HD) efficiency in patients with ESKD who are routinely doing HD in the real world. A total of 50 patients (62.1 ± 10.7 years) with ESKD underwent continuous real-time ECG monitoring (237.4 ± 15.3 min) during HD for HRV using remote monitoring system. Their electrolyte levels were checked before and after HD. We compared HRV according to electrolyte levels. During the monitor, we checked the ECG and electrolyte levels simultaneously a total of 2374 times for all of the patients. Both time and frequency domain HRV were higher when the patients had lower K+ level (<0.5 mEq/L) and P+ level change (<2 mEq/L) before and after HD as compared to those with a higher K+ level (≥0.5 mEq/L) and P+ level change (≥2 mEq/L). Additionally, patients with lower K+ and P+ level change groups had higher incidences of arrhythmic events including atrial/ventricular premature complexes, despite no difference of mean heart rate (p < 0.001). Higher HRV was independently associated with a poorly controlled K+ and P+ level during HD in patients with ESKD. This is consistently evidenced by the independent association between higher HRV, K+ and P+ levels in real time, suggesting that low electrolyte changes before and after HD alone may cause cardiac autonomic dysfunction.

Diffusion-Based Continuous Real-Time Monitoring System for Total Volatile Organic Compounds

In this study, a total volatile organic compound (TVOC) monitoring system was developed and employed for the continuous and real-time monitoring of TVOCs. Gas chromatography (GC) coupled with thermal desorption (TD) and a commercially available portable VOC detector were simultaneously applied to verify the performance of the developed system. Since the developed system was applicable with and without a pump, the effect of the pump on the monitoring performance was also investigated. Overall, the reliable accuracy and precision of the developed TVOC monitoring system were verified in the TVOC concentration of 50~5000 ppbv. Based on the simultaneous analysis with TD-GC, the TVOC monitoring system showed nearly identical performance to the TD-GC system regardless of the presence of the pump. When compared to the commercially available and portable VOC detector equipped with an internal pump, the maximum delayed response of the TVOC monitoring system was 15 min. In the VOC transport in soil, the breakthrough curve was successfully obtained compared to the case with the TD-GC system. Considering the measurement frequency of the TD-GC system, the TVOC monitoring system as a passive monitoring device can be effectively applied to the subsurface area with the TVOC concentration at or above 50 ppbv.

Fault Detection in Active Magnetic Bearings Using Digital Twin Technology

Active magnetic bearings (AMBs) are widely used in different industries to offer non-contact and high-velocity rotational support. The AMB is prone to failures, which may result in system instability and decreased performance. The efficacy and reliability of magnetic bearings can be significantly affected by failures in the sensor and control systems, leading to system imbalance and possible damage. A digital twin is an advanced technology that has been increasingly used in different industrial fields. It allows for the creation and real-time monitoring of virtual replicas of physical systems. This paper proposes a novel method for fault detection of Active Magnetic Bearings (AMBs) using digital twin technology and a neural network. The digital twin model serves as a virtual representation that accurately replicates the actual AMB system’s efficiency and features, allowing continuous real-time monitoring and detection of faults. The conventional neural network (CNN) is used as the primary tool for identifying faults in the Active Magnetic Bearing (AMB) within a digital twin model. Experiments proved the effectiveness and robustness of the suggested approach method to fault detection in the AMB.

Determination of Gait Events and Temporal Gait Parameters for Persons with a Knee-Ankle-Foot Orthosis.

Gait event detection is essential for controlling an orthosis and assessing the patient's gait. In this study, patients wearing an electromechanical (EM) knee-ankle-foot orthosis (KAFO) with a single IMU embedded in the thigh were subjected to gait event detection. The algorithm detected four essential gait events (initial contact (IC), toe off (TO), opposite initial contact (OIC), and opposite toe off (OTO)) and determined important temporal gait parameters such as stance/swing time, symmetry, and single/double limb support. These gait events were evaluated through gait experiments using four force plates on healthy adults and a hemiplegic patient who wore a one-way clutch KAFO and a pneumatic cylinder KAFO. Results showed that the smallest error in gait event detection was found at IC, and the largest error rate was observed at opposite toe off (OTO) with an error rate of -2.8 ± 1.5% in the patient group. Errors in OTO detection resulted in the largest error in determining the single limb support of the patient with an error of 5.0 ± 1.5%. The present study would be beneficial for the real-time continuous monitoring of gait events and temporal gait parameters for persons with an EM KAFO.

Stretchable, Flexible, Breathable, Self-Adhesive Epidermal Hand sEMG Sensor System.

Hand function rehabilitation training typically requires monitoring the activation status of muscles directly related to hand function. However, due to factors such as the small surface area for hand-back electrode placement and significant skin deformation, the continuous real-time monitoring of high-quality surface electromyographic (sEMG) signals on the hand-back skin still poses significant challenges. We report a stretchable, flexible, breathable, and self-adhesive epidermal sEMG sensor system. The optimized serpentine structure exhibits a sufficient stretchability and filling ratio, enabling the high-quality monitoring of signals. The carving design minimizes the distribution of connecting wires, providing more space for electrode reservation. The low-cost fabrication design, combined with the cauterization design, facilitates large-scale production. Integrated with customized wireless data acquisition hardware, it demonstrates the real-time multi-channel sEMG monitoring capability for muscle activation during hand function rehabilitation actions. The sensor provides a new tool for monitoring hand function rehabilitation treatments, assessing rehabilitation outcomes, and researching areas such as prosthetic control.

Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones.

Abstract Mobile health (mHealth) with continuous real-time monitoring is leading the era of digital medical convergence. Wearable devices and smartphones optimized as personalized health management platforms enable disease prediction, prevention, diagnosis, and even treatment. Ubiquitous and accessible medical services offered through mHealth strengthen universal health coverage to facilitate service use without discrimination. This viewpoint investigates the latest trends in mHealth technology, which are comprehensive in terms of form factors and detection targets according to body attachment location and type. Insights and breakthroughs from the perspective of mHealth sensing through a new form factor and sensor-integrated display overcome the problems of existing mHealth by proposing a solution of smartphonization of wearable devices and the wearable deviceization of smartphones. This approach maximizes the infinite potential of stagnant mHealth technology and will present a new milestone leading to the popularization of mHealth. In the postpandemic era, innovative mHealth solutions through the smartphonization of wearable devices and the wearable deviceization of smartphones could become the standard for a new paradigm in the field of digital medicine.

A review of wireless body area networks

&lt;span&gt;The widespread adoption of wireless body area network (WBAN) in healthcare presents opportunities to meet the increasing demand for medical services. WBAN enables continuous real-time (RT) monitoring through biomedical sensor nodes positioned in or around a patient's body, collecting vital physiological data. In addition, WBAN imposes stringent criteria for energy efficiency and reliability throughout data collection and transmission. This review paper places significant emphasis on the fundamental concept and essential characteristics of WBAN technology. First, the WBAN features, including architecture, sensor nodes types and network topology is presented. Then, the study explores a wide variety of communication standards and multiple access (MA) mechanism deployed in this technology. Moreover, it discusses open research and challenging issues such as heterogeneous traffic, quality of services (QoS), energy consumption, reliability, interference management, and human body movements effect. Finally, the paper is concluded, and future directions are identified in this evolving field of human health monitoring technology.&lt;/span&gt;

Biopolymer-protected graphene-Fe3O4 nanocomposite based wearable microneedle sensor: toward real-time continuous monitoring of dopamine.

Neurological disorders can occur in the human body as a result of nano-level variations in the neurotransmitter levels. Patients affected by neuropsychiatric disorders, that are chronic require continuous monitoring of these neurotransmitter levels for effective disease management. The current work focus on developing a highly sensitive and personalized sensor for continuous monitoring of dopamine. Here we propose a wearable microneedle-based electrochemical sensor, to continuously monitor dopamine in interstitial fluid (ISF). A chitosan-protected hybrid nanomaterial Fe3O4-GO composite has been used as a chemical recognition element protected by Nafion antifouling coating layer. The morphological and physiochemical characterizations of the nanocomposite were carried out with XRD, XPS, FESEM, EDAX and FT-IR. The principle of the developed sensor relies on orthogonal detection of dopamine with square wave voltammetry and chronoamperometric techniques. The microneedle sensor array exhibited an attractive analytical performance toward detecting dopamine in phosphate buffer and artificial ISF. The limit of detection (LOD) of the developed sensor was observed to be low, 90 nM in square wave voltammetry and 0.6 μM in chronoamperometric analysis. The practical applicability of the microneedle sensor array has been demonstrated on a skin-mimicking phantom gel model. The microneedle sensor also exhibited good long-term storage stability, reproducibility, and sensitivity. All of these promising results suggest that the proposed microneedle sensor array could be reliable for the continuous monitoring of dopamine.

Large-scale fully printed “Lego Bricks” type wearable sweat sensor for physical activity monitoring

Wearable sweat sensors are becoming increasingly popular for their robust capabilities in non-invasive, dynamic, and continuous real-time monitoring of biological information. Real-time monitoring of large-scale samples is crucial for realizing intelligent health. A major bottleneck for enabling large-scale sweat elucidation is the fabrication of wearable sensors equipped with microfluidic devices and flexible electrodes in a cost-effective, homogeneous performance and rapid large-scale way. Herein, a “Screen+Wax”-printing technique was introduced to prepare these components and construct “Lego Bricks” type wearable sweat sensor sensor to monitor sweat Na+ and K+. Flexible electrode arrays and paper-based microfluidic layers (they act as building blocks) were fabricated on polyethylene terephthalate and paper surfaces, respectively, using screen printing and wax printing. Gold nanoparticles and Na+/K+ ion-selective membranes were modified on the electrode surfaces by electrodeposition and drop coating, respectively. In this work, we highlight the excellent performance of the “Lego Bricks” type wearable sweat sensor in testing the Na+ and K+ imbalance of sweat from different body regions during exercise and, more significantly, to track the physical activity during prolonged exercise under different interventions. Furthermore, the prepared “Lego Bricks” wearable sweat ion electrochemical sensor is demonstrated to be capable of large-scale samples elucidation with outstanding performance and cost-effectiveness, which is expected to deeply integrate sweat monitoring into physical activity, providing an important tool for intelligent health.