Continuous Monitoring System Research Articles

Integration of lean manufacturing system with novel intuitive fuzzy syncretic lean frame work to improve the overall equipment effectiveness

Lean manufacturing embraces the principle of doing more with less by removing non-value-added activities from manufacturing processes to preserve effectiveness, flexibility, and profitability. Predictive maintenance in production stages are more critical since it involves machines which are prone to failure and a post failure maintenance stagnates the production which creates bottlenecks due to machine failures. Hence in this work a novel intuitive fuzzy syncretic lean frame work has been implemented to incorporate the need of intelligent systems. The framework is divided into phases which employs Fuzzy logic with embedded smart sensors for effective utilization of man machine and materials in order to improve the effectiveness of a production floor. A novel time based forecasting is done in the design phase which implements the lean tool Takt time. The manufacturing phase uses the sensors to determine the predictive maintenance of the machines thereby implementing continuous flow as the lean tool and the inspection phase uses smart sensor system for real time continuous monitoring of the machining process thereby incorporating Poka-yoke as a lean tool. Thus by implementing the framework the overall equipment effectiveness is achieved which helps in achieving continuous flow of products and defect free products in any production firm.

How does lead selection impact diagnosis through AI? Implications for continuous monitoring systems using convolutional neural networks

Abstract Background and Objectives Recently, there has been a surge in non-invasive diagnosing methods leveraging the dipolar 12-lead ECG information using Convolutional Neural Networks (CNNs). However, the traditional ECG contains 12 projections of the cardiac dipole in a three-dimensional space can result in redundancy. Furthermore, in continuous monitoring systems such as Holter and wearables, not all 12 leads are consistently available. This study aims to quantify redundancy and determine the best lead configuration for improved classification results using a CNN. It evaluates the level of redundancy in ECG leads and its impact on CNN classification. Methods The degree of redundancy was quantified by means of lead entropies (H) as depicted in Figure 1.A. The standard 12-lead ECG was chosen as the baseline input. From it, various subsets of leads were selected (6, 3, 1 leads) with the objective of attaining maximal orthogonality between leads while minimizing redundancy (Figure 1.B). A 2D-CNN model was trained for multiclass classification: Hypertension (HYP), ST-T interval Changes (STTC), Conduction Disturbances (CD), Myocardial Infarction (MI) and Normal (NORM). The designed inputs were employed to evaluate how redundancy impacts the performance of the CNN (see Figure 1.C). Results Reducing redundancy in input leads led to notable improvements in model prediction for all pathological conditions in terms of Area Under the Curve (AUC). Reducing to 6 and 8 leads were the most suitable input for the CNN (Table 1) increasing by 1.18% in HYP, 0.23% in STTC, 0.52% in CD, and 0.33% in MI. However, a significant reduction in redundancy may result in the loss of essential clinical information for classification, as observed in scenarios involving only 3 or 1 leads. Conclusions When diagnosing pathologies with systems featuring fewer leads, its combinations should aim for maximum orthogonality to reduce redundancy while preserving unique information. By strategically selecting a set of leads with reduced input redundancy, the effectiveness of the standard 12-lead ECG can be improved, thereby enhancing the utility of such continuous monitoring systems for automated diagnosis and also decreasing training and prediction times.

Estimating Methane Emission Durations Using Continuous Monitoring Systems

Estimating Methane Emission Durations Using Continuous Monitoring Systems

Characterization of Staphylococcus aureus isolated from milk samples for their virulence, biofilm, and antimicrobial resistance

The Staphylococcus aureus (S. aureus) one of the important food borne pathogen from milk, which was investigated in this study. The isolates were screened for antimicrobial resistance, enterotoxin genes, biofilm formation, spa typing, coagulase gene polymorphism and accessory gene regulator types. The prevalence of S. aureus in milk samples was 34.4% (89/259). Methicillin resistant S. aureus (MRSA) was found at 27% (24/89) of the isolates, were classified as community acquired based on SCCmec typing. The 24.71% (22/89) isolates demonstrated multiple antimicrobial resistance (MAR) pattern. However, none of the isolates carried vancomycin and mupirocin resistance genes. The isolates were positive for sea and sed enterotoxin genes and exhibited high frequency of biofilm formation. The High-Resolution Melting and conventional spa typing revealed that the isolates had both animal and community-associated S. aureus clustered origins. Coagulase gene polymorphism and agr typing demonstrated variable genotypic patterns. The finding of this study establishes the prevalence of community associated, enterotoxigenic, biofilm forming and antimicrobial resistance among S. aureus from milk in Chennai city. This emphasizing a potential threat to public health which needs a continuous monitoring system and strategies to mitigate their spread across the food chain and achieve food safety.

IoT-Based Smart System for Continuous Patient Health Monitoring

IoT-Based Smart System for Continuous Patient Health Monitoring

Implantable Intraocular Fluorescence Sensor for Visualized Monitoring of Alzheimer's Disease

AbstractWith an increase in the older individuals, the global incidence of Alzheimer's disease (AD) is increasing. Early diagnosis of AD necessitates long‐term monitoring; however, conventional diagnostic methods such as positron emission tomography (PET) and magnetic resonance imaging (MRI) are unsuitable for frequent use because of infrastructure limitations. The eye, connected to the brain via the optic nerve, offers a potential window for monitoring neurodegenerative diseases. This study presents a novel system for continuous intraocular monitoring using a fluorescent aptamer‐conjugated intraocular lens (FAIOL). FAIOL emits fluorescent signals in the presence of beta‐secretase‐1 (BACE1), an enzyme associated with amyloid beta 1–42 production. These signals can be analyzed using mobile phone applications. In vitro, experiments demonstrates that FAIOL responded to BACE1 by detecting low concentrations of the target molecule over extended periods. Ex vivo tests using porcine eyeballs shows an increase in the fluorescence signal intensity by more than 8% within 5 h in the presence of BACE1. The integrated image analysis program reduces sensor noise and provides numerical signal values, facilitating a straightforward interpretation. FAIOL holds significant potential as an innovative platform for the early diagnosis and long‐term monitoring of AD and promises improved patient outcomes through timely intervention and ongoing surveillance.

The Rise of Biochemical Sensors: Technology for Real-Time Health Monitoring (Applications and Future Scope)

Biochemical sensors integrated into wearable health technology represent one of the most transformative innovations of the 21st century. This review delves into the evolution, principles, and real-time applications of biochemical sensors and their role in personalized health monitoring. Wearable biochemical sensors, capable of continuously measuring various biomarkers like glucose, lactate, cortisol, and electrolytes, are revolutionizing healthcare by enabling proactive management of chronic diseases, including diabetes, cardiovascular disorders, and mental health issues. Advances in biosensing technologies, coupled with the use of AI and machine learning algorithms, have enhanced the sensitivity and accuracy of these devices, ensuring that critical health data is available in real-time. From glucose monitoring devices like Abbott's FreeStyle Libre to the latest nanomaterial-based sensors, these innovations are reshaping healthcare delivery by shifting the focus from hospital-centered treatments to patient-centric, continuous monitoring systems. This review provides an in-depth analysis of the technological advancements, challenges, and future directions in biochemical sensors, focusing on key technologies such as electrochemical, optical, and enzymatic sensors. It also highlights the critical role of AI in interpreting the complex data generated by these sensors, paving the way for more efficient diagnostics and predictive healthcare models. Furthermore, the paper explores how these sensors have been applied in infectious disease detection, particularly during the COVID-19 pandemic, and discusses their potential to enhance global health surveillance systems. In conclusion, wearable biochemical sensors represent a significant leap forward in the pursuit of personalized medicine, offering real-time diagnostics and timely interventions for disease management. The future of healthcare is closely tied to the ongoing innovations in sensor technology, with the promise of even more advanced and multifunctional devices on the horizon.

Advances of Triboelectric and Piezoelectric Nanogenerators toward Continuous Monitoring and Multimodal Applications in the New Era

Abstract Benefiting from the widespread potential applications in the era of Internet of Thing (IoT) and metaverse, triboelectric and piezoelectric nanogenerators have attracted considerably increasing attention. Their outstanding characteristics, such as self-powered ability, high output performance, integration compatibility, cost effectiveness, simple configurations and versatile operation modes, could effectively expand the lifetime of vastly distributed wearable, implantable and environmental devices, eventually achieving self-sustainable, maintenance-free and reliable systems. However, current triboelectric/piezoelectric based active (i.e., self-powered) sensors still encounter serious bottlenecks in continuous monitoring and multimodal applications due to their intrinsic limitations of monomodal kinetic response and discontinuous transient output. This work systematically summarizes and evaluates the recent research endeavours to addressing the above challenges, with detailed discussions on the challenge origins, designing strategies, device performance and corresponding diverse applications. Finally, conclusions and outlook regarding the research gap in self-powered continuous multimodal monitoring systems are provided, proposing the necessity of future research development in this field.

Deep Learning‐Coupled Metabolic Heat Integrated Sensing System for Noninvasive Continuous Monitoring of Blood Glucose

The dynamic and continuous monitoring of blood glucose (BG) concentration is crucial for the health management of diabetic patients. Despite its importance, significant challenges remain in the development of effective BG monitoring technologies. Metabolic heat conformation (MHC) offers a promising solution due to its noninvasiveness and reliability. However, progress in MHC technology is hindered by the complexities of multisensor integration and the intricate correlation between MHC and BG. Herein, a wearable BG continuous monitoring device based on metabolic heat integrated sensing (MHIS) is developed, combined with a deep learning network to enable continuous BG detection using MHC principles. The MHIS device integrates a miniaturized circuit and intelligent secondary signal processing, allowing for the simultaneous acquisition and integrated operation of various physiological parameters, including metabolic heat production. By integrating a gate recurrent unit neural network, a model is established to facilitate continuous BG monitoring. The wearable device has a certain accuracy, and when analyzed in comparison with commercial noninvasive glucose meters, the mean absolute relative error meets international standards. The deep learning‐enhanced MHIS system proposed in this work enables noninvasive BG monitoring, paving the way for advancements in personalized healthcare management and offering new opportunities in digital healthcare consultation.

In Vitro Evaluation of a Continuous Potassium Monitoring System in Simulated Dialysis Conditions

In Vitro Evaluation of a Continuous Potassium Monitoring System in Simulated Dialysis Conditions

On the Biases of MERRA-2 Reanalysis and Ground-Based Measurements of Black Carbon Aerosols Over India

The precise measurement of black carbon (BC) and assessment of involved biases are very important considering its serious health and climatic effects. MERRA-2 reanalysis data (modern-era retrospective analysis for research and applications, version 2) of surface black carbon (SBC) and ground measurements by optical photometers (mostly Aethalometer) have been used widely to study the temporal and spatial variation of BC aerosols over India. However, a significant bias (more than 70%) between MERRA-2 SBC concentration and ground measurements of BC (by Aethalometer, referred as MBC (AE)) was observed by Malik and Aggarwal, (2021). In the current study, the possible reasons for this bias are further evaluated by comparing the MERRA-2 SBC and Aethalometer measurements against a reference photometer, i.e., continuous soot monitoring system (COSMOS). Three-year (2020-2022) SBC concentrations of MERRA-2 reanalysis data are compared against simultaneous measurements of BC by COSMOS (MBC (COSMOS)). Additionally, the biases in ground measurements are further investigated by comparing one-month (March 2023) observations of Aethalometer (MBC (AE)). These comparison results revealed a 40% underestimation in MERRA-2 reanalysis during 2020-2022. The higher underestimation (50%) of SBC during increased biomass emissions months (October to May), in contrast to the 25% underestimation during conventional emissions months (June to September), emphasizes the underestimation of biomass emissions inputs in MERRA-2 SBC reanalysis. The initial large bias between raw data of Aethalometer (MBC (AE-raw) = 1.92-1.98 times MBC (COSMOS)) get reduced significantly when correction due to multiple scattering in the filter media and mass absorption cross-section (MAC) are incorporated to correct the Aethalometer data (MBC (AE-corr) = 1.15-1.18 times MBC (COSMOS)). The findings of this study underscore the need to harmonize the correction methods for multiple scattering and redefine the MAC values for Aethalometer measurements as well as updating the anthropogenic emission inventories in MERRA-2 SBC reanalysis.

Continuous Monitoring System of Geologically stored CO<sub>2</sub> at Offshore CCS Sites

Continuous Monitoring System of Geologically stored CO<sub>2</sub> at Offshore CCS Sites

Comparison of the ClearSight™ finger cuff monitor versus invasive arterial blood pressure measurement in elective cardiac surgery patients: a prospective observational study.

To determine the acceptability of the ClearSight™ system (Edwards Lifesciences Corp., Irvine, CA, USA) for continuous blood pressure monitoring during elective cardiac surgery compared with arterial catheterization. We enrolled 30 patients undergoing elective cardiac surgery in a prospective observational study. Blood pressure measurements were recorded every 10 sec intraoperatively. We determined agreement based on the Association for the Advancement of Medical Instrumentation (AAMI) recommendations. Statistical analysis included fixed bias (difference of measurements between methods), percentage error (accuracy between ClearSight measurement and expected measurement from arterial line), and interchangeability (ability to substitute ClearSight monitor without effecting overall outcome of analysis). We used a paired samples t test to compare the time required for placing each monitor. We found fixed bias in the differences between the ClearSight monitor and invasive arterial blood pressure measurement in systolic blood pressure (SBP; mean difference, 8.7; P < 0.001) and diastolic blood pressure (DBP; mean difference, -2.2; P < 0.001), but not in mean arterial pressure (MAP; mean difference, -0.5; P < 0.001). Bland-Altman plots showed that the means of the limits of agreement were greater than 5mm Hg for SBP, DBP, and MAP. The percentage errors for SBP, DBP, and MAP were lower than the cutoff we calculated from the invasive arterial blood pressure measurements. Average interchangeability rates were 38% for SBP, 50% for DBP, and 50% for MAP. Placement of the ClearSight finger cuff was significantly faster compared with arterial catheterization (mean [standard deviation], 1.7 [0.6] min vs 5.6 [4.1] min; P < 0.001). In this prospective observational study, we did not find the ClearSight system to be an acceptable substitute for invasive arterial blood pressure measurement in elective cardiac surgery patients according to AAMI guidelines. Nevertheless, based on statistical standards, there is evidence to suggest otherwise. ClinicalTrials.gov ( NCT05825937 ); first submitted 11 April 2023.

Machine Learning for Advanced Emission Monitoring and Reduction Strategies in Fossil Fuel Power Plants

Fossil fuel power plants are a significant contributor to global carbon dioxide (CO2) and nitrogen oxide (NOx) emissions. Accurate monitoring and effective reduction of these emissions are crucial for mitigating climate change. This systematic review examines the current state of research on the application of machine learning techniques in evaluating the emissions from fossil fuel power plants. This review first briefly introduces the continuous emission monitoring (CEM) systems and predictive emission monitoring (PEM) systems that are commonly used in power plants and highlights that machine learning models can significantly improve PEM systems through their capability to process and interpret large datasets intelligently to transform traditional emission monitoring systems by enhancing their precision, effectiveness, and cost-efficiency. Compared to previously published review articles, the key contribution and innovation in this present review is the discussion of machine learning models in CO2/NOx emissions according to the different algorithms used, including their advantages and disadvantages in a systematic way, which aims to help future researchers to develop more effective machine learning models. The most popular machine learning model includes reinforcement learning, a forward neural network, a long short-term memory neural network, and support vector regression. While each model method has its own advantages and disadvantages, we noted that training data quality, as well as the proper selection of model parameters, plays an important role. The challenges and research gaps, such as model transferability, a deep understanding of the physics of CO2/NOx emissions, and the availability of high-quality data for training machine learning models, are identified, and recommendations as well as potential future research directions to address these challenges are proposed and discussed.

Leading Through Noise: Operating Room Noise Challenges for Staff and Leadership Techniques to Ensure Optimal Operational Performance.

Noise and distractions in the operating room (OR) critically impact surgical performance and patient outcomes, particularly in high-stakes environments such as trauma surgery. While historical hospital environments prioritized quiet to facilitate recovery and reduce stress, contemporary ORs, especially those handling trauma cases, face increasing noise challenges due to advanced surgical instruments, alarms, and staff conversations, often surpassing federal exposure limits. This review investigates OR noise sources, including staff activities and equipment, analyzing their effects on cognitive load, communication, and error rates among healthcare workers. It identifies high-risk scenarios and vulnerable groups, highlighting the necessity for targeted interventions. Key strategies include implementing strict noise control policies, using noise-reducing materials in OR design, and educating staff on noise impacts. Additionally, structured communication protocols and continuous monitoring systems are advocated to enhance operational efficiency and safety. Surgeon leadership is pivotal in balancing assertiveness and empathy to maintain a productive team dynamic. Furthermore, surgeons significantly boost OR efficiency and safety by adopting these protocols, promoting inclusive team dynamics, and applying noise-reduction strategies. These practices safeguard patient care and foster a more collaborative work atmosphere, aligning all team efforts toward optimal patient outcomes. This holistic approach emphasizes the need for continuous improvement and adaptability in surgical practices to meet modern healthcare demands, particularly in trauma surgery's fast-paced, unpredictable realm. Collectively, these measures can enhance patient safety and improve conditions for surgical teams, providing a framework for quieter, more focused OR environments that ultimately elevate surgical outcomes and healthcare quality.

Design and Development of an IoT-Based Embedded System for Continuous Monitoring of Vital Signs

Design and Development of an IoT-Based Embedded System for Continuous Monitoring of Vital Signs

A Passive Perspiration Inspired Wearable Platform for Continuous Glucose Monitoring.

The demand for glucose monitoring devices has witnessed continuous growth from the rising diabetic population. The traditional approach of blood glucose (BG) sensor strip testing generates only intermittent glucose readings. Interstitial fluid-based devices measure glucose dynamically, but their sensing approaches remain either minimally invasive or prone to skin irritation. Here, a sweat glucose monitoring system is presented, which completely operates under rest with no sweat stimulation and can generate real-time BG dynamics. Osmotically driven hydrogels, capillary action with paper microfluidics, and self-powered enzymatic biochemical sensor are used for simultaneous sweat extraction, transport, and glucose monitoring, respectively. The osmotic forces facilitate greater flux inflow and minimize sweat rate fluctuations compared to natural perspiration-based sampling. The epidermal platform is tested on fingertip and forearm under varying physiological conditions. Personalized calibration models are developed and validated to obtain real-time BG information from sweat. The estimated BG concentration showed a good correlation with measured BG concentration, with all values lying in the A+B region of consensus error grid (MARD = 10.56% (fingertip) and 13.17% (forearm)). Overall, the successful execution of such osmotically driven continuous BG monitoring system from passive sweat can be a useful addition to the next-generation continuous sweat glucose monitors.

Assessment of Low-Cost and Higher-End Soil Moisture Sensors across Various Moisture Ranges and Soil Textures.

The accuracy and unit cost of sensors are important factors for a continuous soil moisture monitoring system. This study compares the accuracy of four soil moisture sensors differing in unit costs in coarse-, fine-, and medium-textured soils. The sensor outputs were recorded for the VWC, ranging from 0% to 50%. Low-cost capacitive and resistive sensors were evaluated with and without the external 16-bit analog-to-digital converter ADS1115 to improve their performances without adding much cost. Without ADS1115, using only Arduino's built-in analog-to-digital converter, the low-cost sensors had a maximum RMSE of 4.79% (v/v) for resistive sensors and 3.78% for capacitive sensors in medium-textured soil. The addition of ADS1115 showed improved performance of the low-cost sensors, with a maximum RMSE of 2.64% for resistive sensors and 1.87% for capacitive sensors. The higher-end sensors had an RMSE of up to 1.8% for VH400 and up to 0.95% for the 5TM sensor. The RMSE differences between higher-end and low-cost sensors with the use of ADS1115 were not statistically significant.

Empirical evaluation of feature selection methods for machine learning based intrusion detection in IoT scenarios

This paper delves into the critical need for enhanced security measures within the Internet of Things (IoT) landscape due to inherent vulnerabilities in IoT devices, rendering them susceptible to various forms of cyber-attacks. The study emphasizes the importance of Intrusion Detection Systems (IDS) for continuous threat monitoring. The objective of this study was to conduct a comprehensive evaluation of feature selection (FS) methods using various machine learning (ML) techniques for classifying traffic flows within datasets containing intrusions in IoT environments. An extensive benchmark analysis of ML techniques and FS methods was performed, assessing feature selection under different approaches including Filter Feature Ranking (FFR), Filter-Feature Subset Selection (FSS), and Wrapper-based Feature Selection (WFS). FS becomes pivotal in handling vast IoT data by reducing irrelevant attributes, addressing the curse of dimensionality, enhancing model interpretability, and optimizing resources in devices with limited capacity. Key findings indicate the outperformance for traffic flows classification of certain tree-based algorithms, such as J48 or PART, against other machine learning techniques (naive Bayes, multi-layer perceptron, logistic, adaptive boosting or k-Nearest Neighbors), showcasing a good balance between performance and execution time. FS methods' advantages and drawbacks are discussed, highlighting the main differences in results obtained among different FS approaches. Filter-feature Subset Selection (FSS) approaches such as CFS could be more suitable than Filter Feature Ranking (FFR), which may select correlated attributes, or than Wrapper-based Feature Selection (WFS) methods, which may tailor attribute subsets for specific ML techniques and have lengthy execution times. In any case, reducing attributes via FS has allowed optimization of classification without compromising accuracy. In this study, F1 score classification results above 0.99, along with a reduction of over 60% in the number of attributes, have been achieved in most experiments conducted across four datasets, both in binary and multiclass modes. This work emphasizes the importance of a balanced attribute selection process, taking into account threat detection capabilities and computational complexity.

A real-time cardiac output monitoring system based on photoplethysmography

Abstract Cardiac output is a critical parameter for assessing the cardiovascular system, and thermodilution is commonly used as CO monitoring in clinical settings. Thermodilution cannot be applied in continuous monitoring due to invasion. Photoplethysmography signal originates from the cardiovascular system. We proposed a CO continuous monitoring system based on photoplethysmography and deep learning method. PPG waveform parameters are extracted during the pre-processing stage, and fed into the deep learning model to calculate cardiac output. The system employs an analog front-end chip and an HPS_FPGA architecture to capture PPG signals. The pre-processing and the deep learning model-driven algorithm are designed in Python on the custom Linux for HPS. It realizes the real-time processing of the PPG signals and the continuous monitoring of cardiac output. Validated by an FPGA, the system reported average cardiac output monitoring results of approximately 5.09 L/min at rest and about 7.39 L/min after exercise.