Current Monitoring Systems Research Articles

Clinicians’ perspectives on wearable sensor technology as an alternative bedside monitoring tool in two West African countries

ObjectiveHealthcare facilities in low- and middle-income countries (LMICs), especially in Africa, suffer from a lack of continuous bedside monitoring capability, adversely affecting timely detection of hemodynamic deterioration and the opportunity for life-saving intervention. Wearable device technologies can overcome many of the challenges of conventional bedside monitors and could be viable alternatives. We assessed clinicians’ perspectives on the use of a novel experimental wearable device (“biosensor”) to improve bedside monitoring of pediatric patients in two West African LMICs. MethodsFocus groups were conducted in 3 hospitals (2 in Ghana and 1 in Liberia), in both urban and rural settings and of variable size, to elucidate clinicians’ attitudes about the biosensor and to identify potential implementation needs. The focus group sessions were coded using a constant comparative method. Deductive thematic analysis was applied to pair themes with Consolidated Framework for Implementation Research (CFIR) contextual factors and domains. ResultsFour focus groups were conducted in October 2019, and included 9 physicians, 20 nurses, and 20 community health workers. Fifty-two codes in four thematic areas were linked to 3 CFIR contextual factors and 9 domains. Key themes were durability and cost of the biosensor, hospital setting, and staffing concerns, which were related to the “Inner Setting” and “Characteristics of the Intervention” CFIR contextual factors. Participants, who recognized the limitations of current vital sign monitoring systems, further identified 21 clinical settings in which a biosensor could potentially be useful and expressed willingness to implement the biosensor. ConclusionClinicians who provide care to pediatric patients in two West African LMICs suggested multiple uses of a novel experimental wearable biosensor and expressed willingness to use it for continuous bedside vital sign monitoring. They identified device design (e.g., durability, cost), hospital setting (rural vs urban), and staffing as important factors to consider during further development and implementation.

Technical note: Measurement of the bunch structure of a clinical proton beam using a SiPM coupled to a plastic scintillator with an optical fiber.

Recent proposals of high dose rate plans in protontherapy as well as very short proton bunches may pose problems to current beam monitor systems. There is an increasing demand for real-time proton beam monitoring with high temporal resolution, extended dynamic range and radiation hardness. Plastic scintillators coupled to optical fiber sensors have great potential in this context to become a practical solution towards clinical implementation. In this work, we evaluate the capabilities of a very compact fast plastic scintillator with an optical fiber readout by a SiPM and electronics sensor which has been used to provide information on the time structure at the nanosecond level of a clinical proton beam. A 3×3×3mm3 plastic scintillator (EJ-232Q Eljen Technology) coupled to a 3×3mm2 SiPM (MicroFJ-SMA-30035, Onsemi) has been characterized with a 70 MeV clinical proton beam accelerated in a Proteus One synchrocyclotron. The signal was read out by a high sampling rate oscilloscope (5 GS/s). By exposing the sensor directly to the proton beam, the time beam profile of individual spots was recorded. Measurements of detector signal have been obtained with a time sampling period of 0.8 ns. Proton bunch period (16 ns), spot (10 μs) and interspot (1 ms) time structures could be observed in the time profile of the detector signal amplitude. From this, the RF frequency of the accelerator has been extracted, which is found to be 64 MHz. The proposed system was able to measure the fine time structure of a clinical proton accelerator online and with ns time resolution.

Estimating the economic value of ultrafine particle information: a contingent valuation method

Global concern regarding ultrafine particles (UFPs), which are particulate matter (PM) with a diameter of less than 100nm, is increasing. These particles are difficult to measure using the current methods because their characteristics are different from those of other air pollutants. Therefore, a new monitoring system is required to obtain accurate UFP information, which will raise the financial burden of the government and people. In this study, we estimated the economic value of UFP information by evaluating the willingness-to-pay (WTP) for the UFP monitoring and reporting system. We used the contingent valuation method (CVM) and the one-and-one-half-bounded dichotomous choice (OOHBDC) spike model. We analyzed how the respondents' socio-economic variables, as well as their cognition level of PM, affected their WTP. Therefore, we collected WTP data of 1040 Korean respondents through an online survey. The estimated mean WTP for building a UFP monitoring and reporting system is KRW 6958.55-7222.55 (USD 6.22-6.45) per household per year. We found that people satisfied with the current air pollutant information, and generally possessing relatively greater knowledge of UFPs, have higher WTP for a UFP monitoring and reporting system. We found that people are willing to pay more than the actual installation and operating costs of current air pollution monitoring systems. If the collected UFP data is disclosed in an easily accessible manner, as is current air pollutant data, it will be possible to secure more public acceptance for expanding the UFP monitoring and reporting system nationwide.

Water quality monitoring in recirculating aquaculture systems

AbstractGood water quality in recirculating aquaculture systems (RAS) is crucial for ensuring the successful growth and survival of reared species. So far, there are no regulations for which parameters should be measured in RAS, and each farmer decides which parameters to follow. Traditionally, water quality parameters have been measured at certain intervals with handheld sensors and laboratory analyses, which can be labour intensive. Currently, a variety of sensors and monitoring equipment is available, even for the real‐time monitoring of water quality parameters. Internet of Things‐based systems and artificial intelligence can be applied for the monitoring purposes which allows real‐time measurements and warnings of critical situations. However, many of the modern systems need competent users and require regular maintenance and calibration. Changes in water quality also induces changes in fish behaviour, such as swimming activity, depth, acceleration and water quality can be assessed also based on these changes. In this review, water quality parameters, variety of sensors and monitoring technologies have been summarised to provide an overview of the current monitoring systems for water quality. Additionally, analytical methods for more advanced analyses have also been briefly summarised. Although there are several advanced options available for monitoring the basic water quality parameters, real‐time measurements of more advanced parameters still required require further development.

Hot-Pressing Furnace Current Monitoring and Predictive Maintenance System in Aerospace Applications.

This research combines the application of artificial intelligence in the production equipment fault monitoring of aerospace components. It detects three-phase current abnormalities in large hot-pressing furnaces through smart meters and provides early preventive maintenance. Different anomalies are classified, and a suitable monitoring process algorithm is proposed to improve the overall monitoring quality, accuracy, and stability by applying AI. We also designed a system to present the heater's power consumption and the hot-pressing furnace's fan and visualize the process. Combining artificial intelligence with the experience and technology of professional technicians and researchers to detect and proactively grasp the health of the hot-pressing furnace equipment improves the shortcomings of previous expert systems, achieves long-term stability, and reduces costs. The complete algorithm introduces a model corresponding to the actual production environment, with the best model result being XGBoost with an accuracy of 0.97.

Satellite‐air‐ground integrated multi‐source earth observation and machine learning processing brain for tailings reservoir monitoring and rapid emergency response

AbstractTailings reservoirs are an inevitable part of the production process of metal mines and are typical of post‐mining and post‐industrial lands. Tailings reservoirs are often located in mountainous areas so earth observation and remote sensing is a necessary monitoring means. However, existing observation are generally based on satellite, air or ground platforms, Combined satellite‐air‐ground observation is rare, and the coordination ability of processing methods to cope with it is insufficient. Therefore, the response to sudden major environmental and disaster events is slow and lagging. In this study, inspired by the human brain, a satellite‐air‐ground integrated multi‐source earth observation and machine learning processing system was built up for tailings reservoir stereoscopic monitoring and rapid emergency response (SAGTR). Firstly, the brain‐inspired human visual multimodal data acquisition by satellite‐air‐ground integration platform has been established to break through the traditional single‐source and single‐mode data approach and involves satellite remote sensing images, UAV hyperspectral images, and ground parameters. Moreover, it is unique among current tailings reservoir monitoring systems. In general, our proposed SAGTR framework has promise for improving the comprehensive monitoring and emergency response to tailings reservoir failures.

Developing a geological disaster monitoring system based on electrical prospecting

The functions and performances of current geological disaster monitoring systems have significantly improved in recent years. However, they still possess certain shortcomings, such as lack of deep monitoring functions and being affected by weather, atmosphere, and light. This study aims to combine electrical prospecting with geological disaster monitoring to develop an improved system. The proposed system has a high level of data gathering accuracy and involves deep monitoring. It is not easily affected by natural factors, such as the weather. We designed circuits for the high-precision data acquisition, high-voltage power transmission, and main control and developed an acquisition and monitoring software, which provides adequate technical support for geological disaster monitoring. The system was used to monitor the Lannitan landslide. Our results indicated that the proposed geological disaster monitoring system had stable functionality, high acquisition accuracy, simple operation of the human‒computer interface, and remote monitoring, meeting the requirements for geological disaster monitoring.

Utilization of Bioinorganic Nanodrugs and Nanomaterials for the Control of Infectious Diseases Using Deep Learning.

As one of the main causes of morbidity and mortality, viral infections have a major impact on the well-being and economics of every nation in the globe. The ability to predictably diagnose viral infections improves the provision of good healthcare as well as the control and prevention of these conditions. Nanomaterials have gained widespread usage in the medical industry recently due to the rapid advancement of nanotechnology and their exceptional chemical and physical qualities, such as their small size and synthesized surface properties. The utilization of nanoparticles for illness detection, surveillance, control, preventive, and therapy, such as the treatment of bacterial infections, is referred to as nanomedicine. Nanomedicine is a comprehensive discipline that is founded on the usage of nanotechnology for clinical objectives. Nanoparticles, which have a nanoscale dimension and exhibit highly controllable optical and physical characteristics as well as the ability to bind to a large variety of chemicals, are among the most popular nanomaterials in nanomedicine. A deep learning framework of autoencoder for categorization study on viral infections is built based on actual hospital patient history of viral infections from August 2015 to August 2020. The information comprises of 10,950 cases, comprising outpatients and inpatients, encompassing the infectious diseases. Of such 10,950 instances, training set made up 70% or 7665 instances, and testing data made up 30% or 3285 instances. The data processing was done using the presented recurrent neural network-artificial bee colony (RNN-ABC) method. Sparse data densifying processes are done through the autoencoder to enhance the system learning outcome. The suggested autoencoder system was also evaluated to other widely used models, including support vector machine, logistic regression, random forest, and Naïve Bayes. In comparison to other approaches, the study's findings demonstrate how well the suggested autoencoder model can predict viral diseases. The methods used for this research can aid in removing reported lags in current monitoring systems, hence reducing society's expenses.

Respiratory Monitoring for Anesthesia and Sedation

This article reviews the theory and practice of routine respiratory monitoring during anesthesia and sedation. Oxygen monitoring and capnography methods are reviewed. The current ventilation monitoring system of choice is considered a combination of the pulse oximeter and capnography. Guidelines are provided for monitoring standards.

Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications

Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.

A beam position monitor for electron bunch detection in the presence of a more intense proton bunch for the AWAKE Experiment

The Advanced Proton Driven Plasma Wakefield Experiment (AWAKE) at CERN uses 6 cm long proton bunches extracted from the Super Proton Synchrotron (SPS) at 400 GeV beam energy to drive high gradient plasma wakefields for the acceleration of electron bunches to 2 GeV within a 10 m length. Knowledge and control of the position of both copropagating beams is crucial for the operation of the experiment. Whilst the current electron beam position monitoring system at AWAKE can be used in the absence of the proton beam, the proton bunch signal dominates when both particle bunches are present simultaneously. A new technique based on the generation of Cherenkov diffraction radiation (ChDR) in a dielectric material placed in close proximity to the particle beam has been designed to exploit the large bunch length difference of the particle beams at AWAKE, 200 ps for protons versus a few ps for electrons, such that the electron signal dominates. Hence, this technique would allow for the position measurement of a short electron bunch in the presence of a more intense but longer proton bunch. The design considerations, numerical analysis and plans for tests at the CERN Linear Electron Accelerator for Research (CLEAR) facility are presented.

Development and Evaluation of BLE-Based Room-Level Localization to Improve Hand Hygiene Performance Estimation.

Hand hygiene is one of the most effective ways to prevent infection transmission. However, current electronic monitoring systems are not able to identify adherence to all hand hygiene (HH) guidelines. Location information can play a major role in enhancing HH monitoring resolution. This paper proposes a BLE-based solution to localize healthcare workers inside the patient room. Localization accuracy was evaluated using one to four beacons in a binary (entrance/proximal patient zone) or multiclass (entrance/sink/right side of the bed/left side of the bed) proximity-based positioning problem. Dynamic fingerprints were collected from nine different subjects performing 30 common nursing activities. Extremely randomized trees algorithm achieved the best accuracies of 81% and 71% in the binary and multiclass classifications, respectively. The proposed method can be further used as a proxy for caregiving activity recognition to improve the risk of infection transmission in healthcare settings.

Alati "unapređenja" i "kontrole" u montaži mikro komponenti - studija slučaja

The process of assembling micro components requires careful planning where precise and reliable data collecting is necessary due to a short production cycle time. After determining the shortages of the current data monitoring system, in this paper, by using the "Improve" and "Control" phases of DMAIC cycle, the development and later testing of a new tool for automatic capture and analysis of data obtained from the machine was done. SWOT analysis was used to establish the production site's and current manufacturing execution system strengths and weaknesses, opportunities and threats, which were then utilized to create the newly developed solution accurate and feasible. A tool calibration data was obtained on 9 machines in automotive company's micro component assembly unit. To confirm the accuracy of the data, a RunAtRate test was done on the machines, and by using hypothesis testing, the initial reliability of the new solution was determined. Further testing and comparison revealed that the newly proposed tool recorded 73-80% greater downtime and 40-50% more defective parts than the previously installed system. Accordingly, the usefulness of the proposed Lean Six SIGMA tool is confirmed, and for periodic accuracy checks and further improvement, it is necessary to apply the PDCA cycle.

Improving Health WSN with Distinguished Cloud Computing for Multi-Sensor Analytics

In the twenty-first century, automation has become more prevalent in large part due to the Internet of Things (IoT). The integration of IoT, Wireless Sensor Networks (WSNs) and health monitoring system with multi-cloud environment can optimize a lot of parameters like bandwidth, communication protocols and redundancy of the sensor network. The proposed architecture is introduced keeping in mind the current health infrastructure and monitoring system with different types of network framework. We have also discussed about machine to machine protocols for health monitoring with common entities linked with building communication protocols. The sensors data analytics carried out can sustain multi-level computation is the idea behind this research prospect to give greater depth about direct applicability in multi cloud sensor communication protocols. The offered prototype, when modelled with the unique communication system, produces a more efficient model than the standard way, with a system that is 98 percent more accurate.

Timekeeping and Immediate Monitoring of Employees by Consistently Advocating Time Consciousness and Honesty Using Enhanced Attendance Monitoring System (TIME CATCH Using EAMS)

Attendance management methods that use QR codes and face recognition technology to identify and verify an individual’s features are widely used in many aspects of people’s lives nowadays, notably in pandemic situations where contact-less systems are used. In this paper, the development of enhanced attendance monitoring system using was introduced. The Isabela State University Angadanan Campus has implemented a biometric attendance monitoring system. However, it is limited to the number of employees’ registrations, leave management and generation of Daily Time Record. The biometric system can only store 60 employees in every device, leave application and credits cannot be catered by the system and the Daily Time Record is not auto-generated and auto-formatted. For this reason, an enhanced attendance monitoring system was developed. The system uses a camera and QR Code reader to take attendance electronically, and the attendance records be saved in a database. It can store multiple number of employees, process leave application, manage remaining leave credits, and provide auto-generated Daily Time Record. This method, on the other hand, reduces the requirement for fixed materials and employees to retain records. From the developed system and the gathered data, it has addressed the problem of the conventional way of checking employee’s attendance. The general mean of 4.72 shows that the software quality of the system based on ISO 25010 is found to be functional that the users have strongly agreed on the different characteristics of the system as it addresses the issues in the current attendance monitoring system of ISU.

Development of an intrinsic health risk prediction model for camera-based monitoring of older adults living alone

The number of older adults in Korea is increasing, along with the number of depressed older patients. The causes of depression in older adults include social isolation with negligible interaction with others, irregular nutritional habits, and self-negligence, i.e., they do not engage in any activity. These factors, self-negligence, social isolation, and irregular nutritional habits, are defined as inherent health risks, and in this study, we detected them. These factors can only be derived through long-term monitoring, but the current monitoring system for older adults is severely limited as it focuses only on emergencies, such as “falls.” Therefore, in this study, the goal was to perform long-term monitoring using a camera. In order to capture the physical characteristics of the older adults, the ETRI-Activity3D data were used for training, and the skeleton-based action recognition algorithm Posec3d was used. By defining 90 frames as the time taken for one action, we built a monitoring system to enable long-term monitoring of older adult by performing multiple action detection in one video. A reliable monitoring system, with 98% accuracy, 98% precision, 99% recall, and 98% F1, was successfully established for health monitoring of older adults. This older adult monitoring technology is expected to improve the quality of medical services in a medical environment as well as the objective, activities of daily living test, which does not depend on the observer through daily life detection.

Dependence of Skin-Electrode Contact Impedance on Material and Skin Hydration.

Dry electrodes offer an accessible continuous acquisition of biopotential signals as part of current in-home monitoring systems but often face challenges of high-contact impedance that results in poor signal quality. The performance of dry electrodes could be affected by electrode material and skin hydration. Herein, we investigate these dependencies using a circuit skin-electrode interface model, varying material and hydration in controlled benchtop experiments on a biomimetic skin phantom simulating dry and hydrated skin. Results of the model demonstrate the contribution of the individual components in the circuit to total impedance and assist in understanding the role of electrode material in the mechanistic principle of dry electrodes. Validation was performed by conducting in vivo skin-electrode contact impedance measurements across ten normative human subjects. Further, the impact of the electrode on biopotential signal quality was evaluated by demonstrating an ability to capture clinically relevant electrocardiogram signals by using dry electrodes integrated into a toilet seat cardiovascular monitoring system. Titanium electrodes resulted in better signal quality than stainless steel electrodes. Results suggest that relative permittivity of native oxide of electrode material come into contact with the skin contributes to the interface impedance, and can lead to enhancement in the capacitive coupling of biopotential signals, especially in dry skin individuals.

High-Precision Real-Time Forest Fire Video Detection Using One-Class Model

Machine learning-base fire detection methods play a vital role in the current forest fire monitoring systems. In the literature, fire detection is usually viewed as a two-class (or multi-class) classification task. However, this violates the most foundational hypothesis in machine learning, e.g., independent and identical distribution (i.i.d.), especially for the non-fire samples drawn from a complex forest background. Moreover, for omni-directional video -monitoring, the background is also always changing, which leads this violation to a worse situation. In this work, by relaxing the i.i.d. of non-fire samples, we aim to learn a one-class model that just relies on the fire samples. Considering the requirements of high-precision and real-time detection, training samples are directly constructed on the fire pixels without a complex feature transformation. Additionally, we also provide a batch decision-making strategy to speed up fire detection. This work also includes an extensive experimental comparison on the public forest fire videos, obtained by ground- or unmanned aerial vehicle (UAV)-monitoring cameras. Compared with the state-of-the-art methods, the results show the superiority of our proposal in terms of a high-fire detection rate, low-error warning rate, accurate fire location positioning, and real-time detection.

An energy and leakage current monitoring system for abnormality detection in electrical appliances

Unsafe electrical appliances can be hazardous to humans and can cause electrical fires if not monitored, analyzed, and controlled. The purpose of this study is to monitor the system’s condition, including the electrical properties of the appliances, and to diagnose fault conditions without deploying sensors on individual appliances and analyzing individual sensor data. Using historical data and an acceptable range of normal and leakage currents, we proposed a hybrid model based on multiclass support vector machines (MSVM) integrated with a rule-based classifier (RBC) to determine the changes in leakage currents caused by installed devices at a certain moment. For this, we developed a sensor-based monitoring device with long-range communication to store real-time data in a cloud database. In the modeling process, RBC algorithm is used to diagnose the constructed device fault and overcurrent fault where MSVM is applied for detecting leakage current fault. To conduct an operational field test, the developed device was integrated into some houses. The results demonstrate the effectiveness of the proposed system in terms of electrical safety monitoring and detection. All the collected data were stored in a structured database that could be remotely accessed through the Internet.

Monitoring and control of single-phase electrical systems using IoT based microcontrollers

The internet of things (IoT) has a significant impact on increasing the efficiency and quality of electricity in large power systems. Smart voltage and current monitoring systems (SVCMS) have been developed using a novel method. Using an Arduino Uno microcontroller and a Wi-Fi module, the integrated SVCMS design measures voltage and current sensor findings and sends the processed data to an Android phone. In order to communicate, the global systems for mobile communication (GSM) module is required. When anything goes wrong, an Android app may be used to notify users by text message or email and help them take the right action. It is possible to remotely monitor and control the operation of a gadget using logical programming techniques in Arduino which results easy load control.