Long-term Monitoring System Research Articles

Identification of Climate Change Risks to Surface and Groundwater Resources across the Central African Republic and Resilience Measures

The extensive hydrological and hydrogeological systems across the Central African Republic (CAR) provide essential urban and rural water supplies for the populations across the country. Hydrological and hydrogeological studies during the last 20 years are extremely limited, in part due to the complex security and socio-economic context. As a result, significant uncertainties exist related to water resource availability and infrastructure management. This paper examined the recent evolution of the meteorological, hydrological, and hydrogeological regimes across CAR and the potential future impacts of climate change on surface and water resource availability. This was undertaken by analyzing the available regional hydrometry data and using regional climate change projections to evaluate changes to rainfall and evapotranspiration patterns by 2050. A lack of long-term monitoring data and operational monitoring systems prevents a detailed assessment of these risks at local levels. Although the national scale findings suggest that water resources may slightly increase over the next 40 years, significant local risks remain due to intensifying, increasingly variable rainfall, and increasing demographic pressures. The lack of functional, national-scale water resource monitoring systems contributes to the fragile resilience of the populations across CAR in the face of increased climate-associated risks. Recommendations are included for measures to help characterize and manage the potential risks in the long-term including the implementation of robust water resource monitoring systems

Long-Term Monitoring and Early Warning of Coal Mine Underground Reservoirs—A Case Study in Shigetai Coal Mine

Coal mining is often associated with groundwater pollution and loss, and coal–water conflicts are becoming increasingly prominent in Western China. As a new way to protect mine water, coal mine underground reservoirs (CMURs) have effectively alleviated the water shortage problem in Western China. The CMURs have been in existence for 25 years, but field-monitoring studies on their long-term stability are rare. In this paper, we take the Shigetai coal mine in the Shendong mining area as the research background. Long-term observation of stress, deformation, seepage pressure, and other parameters of 22 dams in five goaves (31201–31205) of the Shigetai coal mine for the whole year of 2022 has been pioneeringly carried out. A novel early-warning model, which incorporates expert evaluations and real-time indicator fluctuations, is proposed to assess the stability of CMURs. The stability characteristics of coal pillar dams (CPDs) and artificial dams (ADs) of CMURs are evaluated by this model, proving the validity and applicability of this model. This model provides theoretical and methodological guidance for long-term monitoring and early-warning systems for CMURs in the Shendong mining area.

Safety of electrophysiological study in atrioventricular block risk stratification in new-onset persistent left bundle brunch block following transcathether aortic valve implantation

Abstract Background A significant percentage of patients develops a persistent complete left bundle branch block (LBBB) following TAVI. Electrophysiological (EP) study could be useful to stratify the risk of atrioventricular block (AVB) in these patients, guiding the need of follow-up and the indication of definite pacemaker. Purpose The primary objective of the study was describing the incidence of bradyarrhythmic events in the 30-day follow-up of patients with new-onset LBBB: AV block with ECG long-term monitoring system (Nuubo) in patients with negative EP study, and ≥ 5 % of ventricular stimulation in patients with positive EP study and pacemaker implantation. Methodology Observational prospective study of consecutive patients without previous permanent pacemaker implantation where transfemoral TAVI was implanted between 2021 and 2023. In case new-onset complete LBBB was present after TAVI, an EP study was performed. EP study was considered positive when His-Ventricule interval was more than 70ms, or after procainamide infusion at 10mg/kg, more than 100ms or more than double the basal HV interval. In case EP was positive, a permanent pacemaker was implanted. In case EP was negative, a 30-day follow-up with ECG long-term monitoring system (Nuubo) was performed. Results In this cohort, 266 patients without pacemaker following TAVI were included. Persistent new-onset left bundle branch block was present in 10,15% of patients (n=27). EP study was positive in 29.62% of patients (n=8), following a definite pacemaker implantation. During follow-up, only one patient (12.5%) was dependent of ventricular stimulation. The group with negative EP study (n=19) were monitored with a 30-day ECG long-term monitoring system (Nuubo). No bradyarrhythmic events were registered in this group. Conclusions Approximately one tenth of patients develop persistent new-onset LBBB after TAVI. Events were seldom in both study groups. Positive EP study patients were dependent of ventricular stimulation in 12.5% of cases, and there was no bradyarrhythmic event registered in EP negative group. EP study could be a safe tool to stratify AVB risk in new-onset LBBB following TAVI.

A Vision and Proof of Concept for New Approach to Monitoring for Safer Future Smart Transportation Systems.

Transportation infrastructure experiences distress due to aging, overuse, and climate changes. To reduce maintenance costs and labor, researchers have developed various structural health monitoring systems. However, the existing systems are designed for short-term monitoring and do not quantify structural parameters. A long-term monitoring system that quantifies structural parameters is needed to improve the quality of monitoring. In this work, a novel Transportation Rf-bAsed Monitoring (TRAM) system is proposed. TRAM is a multi-parameter monitoring system that relies on embeddable backscatter-based, batteryless, and radio-frequency sensors. The system can monitor structural parameters with 3D spatial and temporal information. Laboratory experiments were conducted on a 1D scale to evaluate and examine the sensitivity and reliability of the monitored structural parameters, which are displacement and water content. In contrast to other existing methods, TRAM correlates phase change to the change in concerned parameters, enabling long-term monitoring.

Calibration-free and ready-to-use wearable electroanalytical reporting system (r-WEAR) for long-term remote monitoring of electrolytes markers

A major bottleneck in the development of wearable ion-selective sensors is the inherent conditioning and calibration procedures at the user's end due to the signal's instability and non-uniformity. To address this challenge, we developed a strategy that integrates three interdependent materials and device engineering approaches to realize a Ready-to-use Wearable ElectroAnalytical Reporting system (r-WEAR) for reliable electrolytes monitoring. The strategy collectively utilized (1) finely-configured diffusion-limiting polymers to stabilize the electromotive force in the electrodes, (2) a uniform electrical induction in electrochemical cells to normalize the open-circuit potential (OCP), and (3) an electrical shunt to maintain the OCP across the entire sensor in the r-WEAR. The approaches jointly enable fabrication of homogeneously stable and uniform ion-selective sensors, eliminating common conditioning and calibration practices. As a result, the r-WEAR demonstrated a signal's variation down to ±1.99 mV with a signal drift of 0.5 % per hour (0.12 mV h−1) during a 12-h continuous measurement of 10 sensors and a signal drift as low as 13.3 μV h−1 during storage. On-body evaluations of the r-WEAR for four days without conditioning and re-/calibration further validated the sensor's performance in realistic settings, indicating its remarkable potential for practical usage in a user operation-free manner in wearable healthcare applications.

Deep learning with information fusion and model interpretation for long-term prenatal fetal heart rate data

Long-term fetal heart rate (FHR) monitoring during the antepartum period, increasingly popularized by electronic FHR monitoring, represents a growing approach in FHR monitoring. This kind of continuous monitoring, in contrast to the short-term one, collects an extended period of fetal heart data. This offers a more comprehensive understanding of fetus’s conditions. However, the interpretation of long-term antenatal fetal heart monitoring is still in its early stages, lacking corresponding clinical standards. Furthermore, the substantial amount of data generated by continuous monitoring imposes a significant burden on clinical work when analyzed manually. To address above challenges, this study develops an automatic analysis system named LARA (Long-term Antepartum Risk Analysis system) for continuous FHR monitoring, combining deep learning and information fusion methods. LARA’s core is a well-established convolutional neural network (CNN) model. It processes long-term FHR data as input and generates a Risk Distribution Map (RDM) and Risk Index (RI) as the analysis results. We evaluate LARA on inner test dataset, the performance metrics are as follows: AUC 0.872, accuracy 0.816, specificity 0.811, sensitivity 0.806, precision 0.271, and F1 score 0.415. In our study, we observe that long-term FHR monitoring data with higher RI is more likely to result in adverse outcomes (p = 0.0021). In conclusion, this study introduces LARA, the first automated analysis system for long-term FHR monitoring, initiating the further explorations into its clinical value in the future.

Hardware-software implementation of a local Wi-Fi network for the transmission of biomedical signals

The object of this study is a wireless local Wi-Fi network for broadcasting biomedical signals, its structure, and principles of construction. The task of minimizing the power consumption of a Wi-Fi transmitter has been addressed, which provides the possibility of building a wireless system for long-term monitoring of biomedical signals. As a result, a functional diagram of a wireless Holter monitoring system based on an ESP32 microcontroller was constructed, which includes a subsystem for setting up and diagnosing system units using MATLAB software packages, an ECG signal generator, and a multifunctional PCIe board from National Instruments. Evaluation criteria and methods for minimizing power consumption by an autonomous Wi-Fi transmitter have been proposed. Methods for synchronizing the working cycles of the transmitter and receiver of the Holter monitoring system were determined. A procedure for determining the optimal biosignal measurement frequency is presented, at which the distortion of ECG signals would be minimal, which means that the signal could be transmitted without losses. The concept of constructing an algorithm for implementing a program for a Wi-Fi transmitter has been developed, ensuring parallel execution of ECG signal measurement operations and their transmission over a local network. The data from semi-naturalistic tests with an experimental Holter monitoring system with a pre-setup subsystem and using external measuring devices, a computer, and the MATLAB software environment are presented. A comparative analysis of the experimental data with primary ECG signals and ECG signals at the receiver output showed a fairly stable correspondence between the input and output ECG signals. The proposed algorithms make it possible to reduce the average current consumption of the ESP32 microcontroller to 50.5 mA. The results of the study demonstrate the possibility of constructing an energy-efficient wireless system for long-term monitoring of biomedical signals based on the Wi-Fi interface

Real-time observation of nasal cycle during sleep with polysomnography.

To investigate the nasal cycle (NC) during sleep in healthy individuals without nasal obstruction or obstructive sleep apnoea via a flexible wearable respiratory monitoring system in a continuous and real-time manner. NC during sleep was continuously measured in 30 healthy individuals (15 women, 15 men) via long-term sleep respiratory monitoring system, while sleep stage and body position were simultaneously recorded via polysomnography (PSG). The number of NC transitions and positional changes were documented each night. Additionally, time intervals between NC transitions and their closest positional changes during sleep were meticulously recorded to investigate potential correlations between them. A total of 86.7% of the participants displayed the classic NC, with a mean duration of 6.43 ± 2.33h. Nightly observations revealed an average occurrence of 2.19 ± 0.40 NC transitions, predominantly occurring during REM stage (68.4%), and 9.15 ± 7.77 postural changes. Analysis of the intervals between NC transitions and positional changes revealed an average absolute value of 27.72 ± 10.85min, with a substantial 56.4% exceeding 30min, indicating a non-obvious sequence order among them. NC can be measured in a continuous and real-time manner, the transitions occur mainly during the REM stage. However, we have not identified a clear correlation between NC transition and positional change.

Over 25-year monitoring of the Tsing Ma suspension bridge in Hong Kong

AbstractBridges in service are subjected to environmental and load actions, but their status and conditions are typically unknown. Health monitoring systems have been installed on long-span bridges to monitor their loads and the associated responses in real time. Since 1997, the Tsing Ma suspension bridge in Hong Kong has been the world’s first of the type equipped with a long-term health monitoring system. For the first time, this study reports the first-hand field monitoring data of the bridge from 1997 to 2022. The 26-year data provide an invaluable and rare opportunity to examine the long-term characteristics of the loads, bridge responses, and their relationships, thereby enabling the assessment of the bridge’s load evolution and structural condition over time. Results show that traffic loads have remained stable after 2007, highway vehicles kept increasing until the COVID-19 pandemic in 2020, the annual maximum deck temperature continued to increase at a rate of 0.51 °C/decade, typhoon durations increased by 2.5 h/year, and monsoon speeds decreased and became dispersed and variable. For the bridge responses, deck displacement is governed by the varying temperature. Natural frequencies in the past 26 years were almost unchanged. The overall condition of the bridge is very satisfactory. Current status and recent update of the health monitoring system are also reported. Lastly, prospects of bridge health monitoring are discussed. This study is the first to report the over one-quarter century status of a structural health monitoring system and the behavior of a long-span suspension bridge. This research provides a benchmark for many other bridge monitoring systems worldwide.

Historic Built Environment Assessment and Management by Deep Learning Techniques: A Scoping Review

Recent advancements in digital technologies and automated analysis techniques applied to Historic Built Environment (HBE) demonstrate significant advantages in efficiently collecting and interpreting data for building conservation activities. Integrating digital image processing through Artificial Intelligence approaches further streamlines data analysis for diagnostic assessments. In this context, this paper presents a scoping review based on Scopus and Web of Science databases, following the PRISMA protocol, focusing on applying Deep Learning (DL) architectures for image-based classification of decay phenomena in the HBE, aiming to explore potential implementations in decision support system. From the literature screening process, 29 selected articles were analyzed according to methods for identifying buildings’ surface deterioration, cracks, and post-disaster damage at a district scale, with a particular focus on the innovative DL architectures developed, the accuracy of results obtained, and the classification methods adopted to understand limitations and strengths. The results highlight current research trends and the potential of DL approaches for diagnostic purposes in the built heritage conservation field, evaluating methods and tools for data acquisition and real-time monitoring, and emphasizing the advantages of implementing the adopted techniques in interoperable environments for information sharing among stakeholders. Future challenges involve implementing DL models in mobile apps, using sensors and IoT systems for on-site defect detection and long-term monitoring, integrating multimodal data from non-destructive inspection techniques, and establishing direct connections between data, intervention strategies, timing, and costs, thereby improving heritage diagnosis and management practices.

Singing in the rain! Climate constraints on the occurrence of indri's song.

The study of how animals adapt their behaviors depending on weather variables has gained particular significance in the context of climate change. This exploration offers insights into endangered species' potential threats and provides information on the direction to take in conservation activities. In this context, noninvasive, cost-effective, and potentially long-term monitoring systems, such as Passive Acoustic Monitoring (PAM), become particularly appropriate. Our study investigates the relationship between weather variables and the vocal behavior of Indri indri, the sole singing lemur species, within Madagascar's Maromizaha New Protected Area. Using PAM, we explore the factors shaping the vocalization patterns of this primate species in response to some environmental factors in their natural habitat. Analysis of an extensive audio data set collected across different years revealed the differential influence of temperature and precipitation on Indri indri vocal activity. We found that rainfall negatively influenced the emission of the vocalizations while warmer temperatures correlated with a greater emission of songs. The various environmental factors we considered also affected the timing of vocal emissions, showing the same pattern. Furthermore, our study confirms, once again, the strength of PAM as a valuable tool for studying vocal animal communication quickly, giving us information about long-term behavioral patterns that would be difficult to get in other ways. This research gives us further valuable information about how indris use vocalizations in their environment and how they adjust to environmental changes.

Multifunctional sensors for surface synchronized monitoring of Pressure/Biopotential signals and motion recognition

Surface biopotential dry electrodes are an important part of long-term healthcare health monitoring systems. During long-term dynamic monitoring, pressure fluctuations between the skin/electrode can affect the acquisition of ECG signals and reduce the stability of the system. In this study, by printing an interactive circuit on the back of the Fructus xanthii-inspired barb structure (FXbs) Ag/AgCl-TPU dry electrodes, the micro-nanocone PANI/PMMA flexible conductive film is encapsulated on the back of the FXbs dry electrode, and a multifunctional sensor and monitoring clothing are constructed, which can synchronously collect pressure signals and ECG signals, and can monitor human movements. In the simulation instrument and human skin surface, the influence of pressure fluctuation on the quality of ECG signal acquisition under different pressure, speed, and motion states is explored. In the simulation instrument, when the change rate of piezoresistive reaches the maximum value of 51.41 %, the fluctuation range of ECG is the smallest, which is 0.99 mV. On the surface of human skin, the change rate of piezoresistive and the fluctuation range of ECG in the turning state reach the maximum, which is 5.02 % and 5.99 mV, respectively. It can provide a reference to study the influence of pressure fluctuation on ECG signals on the surface of human skin, and at the same time, it can provide an experimental method and basis for the study of the dynamic noise mechanism at the electrode/electrolyte interface.

Home Activity Recognition for Rural Elderly Based on Deep Learning and Smartphone Sensors

With the exacerbation of the rural aging population trend, home-based health monitoring for the rural elderly has become a societal focal point, demanding an effective technological means to elevate the level of rural elderly health management. However, traditional algorithms for monitoring rural elderly behavior face myriad challenges, such as effectively capturing temporal and spatial features. Consequently, addressing the need to enhance the accuracy and robustness of rural elderly behavior recognition has become an urgent problem to solve. This study responds to this challenge by comprehensively employing deep learning and temporal modeling techniques, designing, and validating a short-term and long-term dual-layer home-based health monitoring system for the rural elderly.In the short-term layer, the model utilizes smartphones to collect health information from the rural elderly in various ways and performs real-time anomaly behavior detection.

Ultralow-Power Single-Sensor-Based E-Nose System Powered by Duty Cycling and Deep Learning for Real-Time Gas Identification.

This study presents a novel, ultralow-power single-sensor-based electronic nose (e-nose) system for real-time gas identification, distinguishing itself from conventional sensor-array-based e-nose systems, whose power consumption and cost increase with the number of sensors. Our system employs a single metal oxide semiconductor (MOS) sensor built on a suspended 1D nanoheater, driven by duty cycling─characterized by repeated pulsed power inputs. The sensor's ultrafast thermal response, enabled by its small size, effectively decouples the effects of temperature and surface charge exchange on the MOS nanomaterial's conductivity. This provides distinct sensing signals that alternate between responses coupled with and decoupled from the thermally enhanced conductivity, all within a single time domain during duty cycling. The magnitude and ratio of these dual responses vary depending on the gas type and concentration, facilitating the early stage gas identification of five gas types within 30 s via a convolutional neural network (classification accuracy = 93.9%, concentration regression error = 19.8%). Additionally, the duty-cycling mode significantly reduces power consumption by up to 90%, lowering it to 160 μW to heat the sensor to 250 °C. Manufactured using only wafer-level batch microfabrication processes, this innovative e-nose system promises the facile implementation of battery-driven, long-term, and cost-effective IoT monitoring systems.

Towards automatic home-based sleep apnea estimation using deep learning

Apnea and hypopnea are common sleep disorders characterized by the obstruction of the airways. Polysomnography (PSG) is a sleep study typically used to compute the Apnea-Hypopnea Index (AHI), the number of times a person has apnea or certain types of hypopnea per hour of sleep, and diagnose the severity of the sleep disorder. Early detection and treatment of apnea can significantly reduce morbidity and mortality. However, long-term PSG monitoring is unfeasible as it is costly and uncomfortable for patients. To address these issues, we propose a method, named DRIVEN, to estimate AHI at home from wearable devices and detect when apnea, hypopnea, and periods of wakefulness occur throughout the night. The method can therefore assist physicians in diagnosing the severity of apneas. Patients can wear a single sensor or a combination of sensors that can be easily measured at home: abdominal movement, thoracic movement, or pulse oximetry. For example, using only two sensors, DRIVEN correctly classifies 72.4% of all test patients into one of the four AHI classes, with 99.3% either correctly classified or placed one class away from the true one. This is a reasonable trade-off between the model’s performance and the patient’s comfort. We use publicly available data from three large sleep studies with a total of 14,370 recordings. DRIVEN consists of a combination of deep convolutional neural networks and a light-gradient-boost machine for classification. It can be implemented for automatic estimation of AHI in unsupervised long-term home monitoring systems, reducing costs to healthcare systems and improving patient care.

Analysis of natural occurring radionuclide activity concentration of fish in west coast waters of Sabah, Malaysia

Abstract Fish is an important source of protein in human diets, but concerns arise due to natural radionuclide contamination in food and water sources. The study aimed to assess the concentration radionuclides activities (232Th, 238U, and 40K) in three commercial fish species from west coast waters of Sabah, Malaysia. Additionally, the annual effective dose and cancer risk for adults consuming these fish were evaluated. The concentration analysis was conducted using the inductively coupled plasma-mass spectrometer (ICP-MS) technique. The average concentration levels of radionuclide activity detected, and the annual effective dose was estimated to be much lower than UNSCEAR recommendations. The collective effective dose was estimated at 1.88 μSv y−1 for 232Th, 1.11 μSv y−1 for 238U, and 15.12 μSv y−1 for 40K. The cancer risk for adults from the annual effective dose was also found to be much lower than UNSCEAR and ICRP recommendations. Based on the study’s findings, consuming fish from west coast waters of Sabah is deemed safe. However, it is recommended to establish a long-term monitoring system for radionuclide bioaccumulation in fish to gather valuable information for assessing the potential health risks associated with radionuclides in Malaysia, particularly in the Sabah.

Blockchain and IoT integration for secure short-term and long-term air quality monitoring system using optimized neural network.

Accurate air pollution prediction is vital for residents' well-being. This research introduces a secure air quality monitoring system using neural networks and blockchain for robust analysis, precise predictions, and early pollution detection. Blockchain guarantees data integrity, security, and transparency. Goals include real-time air quality data, secure blockchain recording, and enhanced safety through informed decisions. The research integrates blockchain and IoT for short- and long-term air quality monitoring, utilizing an optimized neural network. IoT sensors collect PM2.5, PM10, CO, NO2, and SO2, processed through noise removal and normalization, with feature extraction using N-tuple contrastive learning. Predictions utilize Graph attention-based deep Residual shrinkage Network and Bidirectional long short Term Memory (GRNBTM) categorized into five levels. An adaptive bowerbird algorithm optimizes parameters, reducing computational complexity. Blockchain integration ensures secure, tamper-proof data storage with a lightweight consensus-based algorithm. The GRNBTM model's air quality monitoring performance is extensively simulated and analyzed at 30-min, 2-h, 1-day, and 1-month intervals, demonstrating superior performance over existing techniques.

Long-term monitoring system for pressure in extrahepatic bile ducts with automatic regulation

Currently, an urgent and unresolved problem is the automation of pressure monitoring in the extrahepatic bile ducts in the postoperative period. Obstruction of the bile ducts associated with narrowing or obstruction of the bile ducts leads to the development of life-threatening conditions such as bile peritonitis and subhepatic abscess. A system is described that allows for the automation of pressure monitoring in the extrahepatic bile ducts.

A Novel Wireless Power Transfer System for Long-Term and Real-Time Monitoring of Subsurface CO2 Storage

A Novel Wireless Power Transfer System for Long-Term and Real-Time Monitoring of Subsurface CO₂ Storage

Simulation and prediction of vortex-induced vibration of a long suspension bridge using SHM-based digital twin technology

Although wind tunnel tests have already become a design basis for improving the stability of long-span bridges under the action of wind loads, vortex-induced vibration (VIV) events of in-service long-span bridges have still been frequently reported in recent years. The reasons behind this could be attributed to the limitations and uncertainties involved in wind tunnel tests as well as the changes and degradations of in-service bridges. In consideration that most long-span bridges have now been equipped with long-term structural health monitoring (SHM) systems and that digital twins could provide a new solution for simulating and predicting the reality, this study aims at developing a digital twin for VIV of a long suspension bridge to simulate and predict its VIV. The design document-based finite element (FE) model of the bridge is first updated using the measured dynamic characteristics. The numerical model for VIV of the bridge is then established using the vortex-induced force (VIF) model established based on wind tunnel test results. The numerical model together with the VIF model are finally updated against the measured vortex-induced responses (VIR) to produce a digital twin for VIV of the bridge. The relationships between the parameters in the VIF model and wind characteristics are further investigated to enable the digital twin to predict future VIV events. The accuracy of the digital twin for predicting the future VIV and the influence of the VIF parameters obtained from wind tunnel tests on the prediction accuracy both are confirmed through comparisons between the predicted and measured results.