Deployment Of Monitoring Research Articles

A case study: deployment of real-time smart city monitoring using YOLOv7 in Selangor cyber valley

A case study: deployment of real-time smart city monitoring using YOLOv7 in Selangor cyber valley

The sphere of exposure: centering user experience in community science air monitoring

Community science has increased in popularity in communities where residents hope to investigate the relationship between environmental issues and personal health. This study partnered with neighborhoods in the most polluted residential zip code in the US to conduct community science air quality monitoring. We conducted 60 semi-structured interviews after two monitoring deployments to understand participants’ subjective experiences of pollution exposure, their engagement with low-cost air quality monitors, and their data interpretation. We utilize the environmental health concept ‘exposure experience’ to analyze how participants use personal monitors, understand their data, and reinterpret their pollution exposure as a result. We further explore how participants’ understandings are circumscribed by the technological features of low-cost monitors. We find that participants adopt both protective and mitigating behavioral changes based on information gained from personal experiments and hypothesis testing while using the monitors. Of their own accord, 40% of participants in this study adopted mitigation behaviors after identifying sources that impacted their personal air quality. Our analysis reveals that real-time data accessibility through low-cost monitors builds exposure awareness and enables residents of environmental justice communities to test, validate, or invalidate sensory experiences and challenge existing assumptions. These findings point to specific pathways for using low-cost monitors to support individual decision-making and contribute to behavioral change. Findings also identify some limitations of low-cost monitors; designers of low-cost monitors should consider how composite Air Quality Scores may encourage community scientists to equally value scientifically-established pollutants (e.g., PM) with less scientifically-established pollutants (e.g., TVOCs), without additional scientific training and health-related information.

AI-powered Strategies for Optimizing Waste Management in Smart Cities in Beijing

The study investigates the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies into Beijing's waste management system, emphasizing their effects on operational efficiency, environmental sustainability, and economic feasibility. The deployment of AI-driven route optimization and IoT-enabled real-time monitoring resulted in a 25% reduction in waste collection trips and a 30% decrease in waste overflow incidents. These advancements led to notable reductions in fuel consumption and environmental impact, while an economic analysis projected a Net Present Value (NPV) of $3.5 million over a 10-year period, affirming the financial benefits of these technologies. The findings highlight the pivotal role of AI and IoT in optimizing urban waste management practices. The study offers policy recommendations for the phased and strategic adoption of these technologies within Beijing, with the potential to enhance efficiency and contribute to the city’s sustainability objectives. Future research is advised to examine the long-term sustainability of AI-driven waste management strategies and assess the applicability of these technologies in diverse urban environments.

1907P Deployment of remote patient monitoring in older patients: A real-world experience from 2419 patients across 58 centres in France and Belgium

1907P Deployment of remote patient monitoring in older patients: A real-world experience from 2419 patients across 58 centres in France and Belgium

A training-free non-intrusive air conditioning load monitoring method based on fuzzy comprehensive evaluation

Air conditioner (AC) is a grid friendly load, monitoring its operating status plays a key role in evaluating user side potential for participation in demand response. With continuous innovations in AC technology, the AC operation characteristics have shown increasing complexity, which poses significant challenges for non-intrusive AC load monitoring. Furthermore, obtaining labeled training data for target scenarios is often challenging, which hinders deployment of many Non-Intrusive Load Monitoring (NILM) methods in practice. In this regard, this article proposes a training-free non-intrusive AC load monitoring method based on fuzzy comprehensive evaluation (FCE), which is very easy to deploy and has good robustness. Firstly, considering various influence factors, a FCE model is constructed based on common knowledge about AC. This model simulates the manual labeling process to locate AC operation periods. Secondly, an AC power estimation method based on weighted filtering is established, in which weighted filter is used to eliminate the non-AC load components that may overlap during AC operation periods. Specifically, an optimization model is constructed to make the cumulative power of the AC events in its working cycle approach zero, with the filter weight vector as the objective variable, in which a heuristic greedy algorithm is used to find the optimal filter weight vector. When multiple feasible solutions with similar objective function values are derived in the solving process, the probability of load events appearing in AC and non-AC operation periods are compared. The final solution is selected, taking into account the likelihood of each feasible solution corresponding to a load event sequence belonging to AC load. The testing results on the PecanStreet dataset and multiple real-world measured datasets prove the effectiveness of the proposed method in monitoring complex ACs with poor waveform consistency.

The growth and emergence of potentially dangerous glacier lakes in Astore Basin, Western Himalaya during 1993–2021

The recent retreat of glaciers in High Mountain Asia is a major issue for downstream communities. Similarly, glaciers in the Astore Basin are melting, causing glacial lakes to expand faster, new lakes to form, and increasing the risk of glacial lakes outburst floods (GLOFs). The present study uses Landsat data from 1993 to 2021 to explore seasonal and decadal changes in glacier lakes, which are validated using an in situ differential Global Positioning System (dGPS). During the ablation period (June - October) of 2021, we observed a five-fold increase (18 to 100) in the number of glacier lakes, as well as a six-fold increase (0.62 to 3.86 km2) in lakes larger than 0.01 km2. Over the last decade, from 2011 to 2020, the number of potentially dangerous glacial lakes (PDGLs) has doubled. GLOF risk must be reduced through continual monitoring of these lakes. Prioritizing the deployment of GLOF monitoring and early warning systems, as well as sustainable water management practices, is critical for mitigation and adaptation measures in mountainous regions.

Sufficiency for PSS tracking gait disorders in multiple sclerosis: A managerial perspective

This study primarily aimed to explore the capabilities of digitalisation in the healthcare context, focusing on a specific disease. In this case, the study examined the potential of remote monitoring of gait to address the sensitivity of multiple sclerosis progression to gait characteristics by adopting a non-invasive approach to remotely quantify gait disturbances in a patient's daily life. To better understand the managerial aspects associated with this approach, the researchers conducted a literature review along with a set of semi-structured interviews. The target population included MS patients as well as the key agents involved in their care: patients' family members, neurologists, MS nurses, physiotherapists, medical directors, and pharmacist. The study identifies the perceived barriers and drivers that could contribute to the successful deployment of PSS remote gait monitoring as a healthcare service: i) At mega-level governance. Implications on privacy and security data are notable barriers missing on the speech. ii) At macro level, funding is highlighted as main barrier. The cost and lack of health system subsidies may render initiatives unsustainable, as emphasised by the interviewees. iii) At meso level, useable data is recognised as a driver. The data collection process can align with diverse interests to create value and business opportunities for the ecosystem actors, enhance care, attract stakeholders, such as insurers and pharma, and form partnerships. iv) At micro-level processes, we find two potential barriers: wearable device and app usability (comfort, navigation, efficiency) and organisational/behavioural aspects (training, digital affinity, skills), which are crucial for value creation in innovation ecosystems among patients and healthcare professionals. Finally, we find an interesting gap in the literature and interviews. Stakeholders' limited awareness of technological demands, especially from information technologies, for a successful long-term service, can be consider two key barriers for PSS.

Composite encoder–decoder network for rapid bridge damage assessment using long-term monitoring acceleration data

In recent years, the deployment of structural health monitoring (SHM) systems has become paramount for safeguarding critical infrastructures. Notwithstanding, the development of an unsupervised deep learning framework capable of learning from long-term sensor data remains a critical challenge, particularly in accurately assessing the exact damage location. This study addresses this gap by proposing a novel approach for rapid bridge damage assessment. The proposed method employed a deep overcomplete encoder–decoder network (DOEDN) to reconstruct the acceleration data acquired from each sensor node on the bridge. The reconstruction losses generated by the DOEDN framework are then used as damage-sensitive features. Additionally, a damage indicator based on Gaussian processes is introduced to assess the damage location and evaluate its severity. The performance and sensitivity of the proposed DOEDN framework are evaluated through long-term monitoring acceleration data from a numerical highway bridge model and the well-known full-scale Z24 bridge. Furthermore, comparative assessments against the regular deep undercomplete encoder–decoder network are conducted using metrics including mean absolute error, coefficient of determination (R2), and mean intersection over union. The results show that the proposed DOEDN framework can reasonably assess the damage location and evaluate its severity across various structural scenarios in the bridge, even in the presence of temperature variations, thus providing a practical and effective solution for bridge health monitoring.

Urban Seismic Networks: A Worldwide Review

Seismic networks in urban areas today represent key infrastructure to better address the tasks of earthquake preparation and mitigation in the pre-event phase, and are an important knowledge tool supporting disaster risk management during seismic crises and post-disaster recovery. In the last fifteen years, a decrease in instrumentation costs and the development of new low-cost devices have enhanced the deployment of several monitoring and experimental networks worldwide. This paper conducts a review of scientific work that refer to the deployment of Urban Seismic Networks (USN) in order to define the current state of the art. We collected a list of more than one hundred USNs worldwide that were operative within the period from 1994–2023. For each USN, we report the locations and objectives along with information about the timing, coverage, geometry, and technical characteristics (sensors and transmission). By reviewing all these aspects, this paper offers important insights to provide guidelines for new implementations, bearing in mind that the interest in monitoring urban areas is expected to continue to increase in the near future driven by population growth in urbanized areas.

Telemonitoring – Regulations and Opportunities

Abstract Regulation compliance, privacy, and security issues all need to be taken into consideration while using remote patient monitoring. This article on current developments in remote healthcare and monitoring sheds light on contactless techniques and draws attention to the difficulties still facing the deployment of remote patient monitoring. It is summarized how remote patient monitoring is already being used, paying special attention to Germany and its regulatory framework.

Multi-Objective Optimal Deployment of Road Traffic Monitoring Cameras: A Case Study in Wujiang, China

This study presents a multi-objective optimal framework for deploying traffic monitoring cameras at road networks. Compared with previous studies that focused on addressing single traffic problem such as OD estimation, link flow observation, path flow reconstruction, and travel time estimation, this study aims to address a comprehensive traffic management problem, including crash prevention, traffic violation governance, and traffic efficiency improvement. First, a potential principle for selecting the location of traffic monitoring deployment is determined, taking into account the key signalized intersections, areas prone to traffic congestion, crash-prone spots, and areas prone to traffic violations. Then, a multi-objective optimal model is developed to minimize the ATFM (i.e., average traffic volume of each five minutes), TCF (i.e., traffic crash frequency), and TVF (i.e., traffic violation frequency) while adhering to cost constraints. Finally, RVEA and NSGA-II algorithms are used to solve the multi-objective optimal model, respectively, and a comprehensive metric is proposed to evaluate the deployment schemes. The case study results demonstrate that the solutions obtained by the RVEA algorithm outperform those of the NSGA-II algorithm, and the best traffic monitoring deployment rate is 62.79%, under cost constraints. In addition, the comparison using the FAHP method also illustrates that the RVEA scheme is superior to the NSGA-II scheme. The above research results could potentially be used to optimize the locations of traffic cameras in road networks, which help to improve traffic management.

The Complexity of Transferring Remote Monitoring and Virtual Care Technology Between Countries: Lessons From an International Workshop.

International deployment of remote monitoring and virtual care (RMVC) technologies would efficiently harness their positive impact on outcomes. Since Canada and the United Kingdom have similar populations, health care systems, and digital health landscapes, transferring digital health innovations between them should be relatively straightforward. Yet examples of successful attempts are scarce. In a workshop, we identified 6 differences that may complicate RMVC transfer between Canada and the United Kingdom and provided recommendations for addressing them. These key differences include (1) minority groups, (2) physical geography, (3) clinical pathways, (4) value propositions, (5) governmental priorities and support for digital innovation, and (6) regulatory pathways. We detail 4 broad recommendations to plan for sustainability, including the need to formally consider how highlighted country-specific recommendations may impact RMVC and contingency planning to overcome challenges; the need to map which pathways are available as an innovator to support cross-country transfer; the need to report on and apply learnings from regulatory barriers and facilitators so that everyone may benefit; and the need to explore existing guidance to successfully transfer digital health solutions while developing further guidance (eg, extending the nonadoption, abandonment, scale-up, spread, sustainability framework for cross-country transfer). Finally, we present an ecosystem readiness checklist. Considering these recommendations will contribute to successful international deployment and an increased positive impact of RMVC technologies. Future directions should consider characterizing additional complexities associated with global transfer.

Assessment of potentially toxic elements in groundwater through interpolation, pollution indices, and chemometric techniques in Dehradun in Uttarakhand State.

Providing safe drinking water for the entire world's population is essential for ensuring sustainable development. The presence of harmful compounds in aquifers, majorly toxic elements, is a serious environmental concern around the globe. This research aimed to quantify for the initial period the amounts of toxic elements in freshwater in the Dehradun Industrial Region of Uttrakhand, India, as well as the associated health risks. The PTEs (potentially toxic elements) Fe, Cd, Mn, Cu, Cr and Pb, Zn, Ni is measured by AAS and compared to BIS and WHO requirements for drinking safety. The order of mean trace element values in all groundwater samples were determined as Fe > Zn > Cu > Ni > Co > Cd > Pb. HPI was discovered to be higher than high class during the research period (HPI > 30), but under the severe contamination criterion of 100. Iron's MI and PI values were consistently over the threshold limit during the research period, and certain toxic elements were discovered exceptionally near the threshold limit, indicating a severe future influence on groundwater quality. According to PCA (principal component analysis), CM (correlation matrix), and potential health hazard, maximum levels of toxic elements in groundwater in the Dehradun region are attributed to land use patterns, anthropogenic activity, industrial activity, fertilizer and pesticide leaching, and residential waste into the aquifer system. The findings of this study could aid local planners and policymakers in preventing health risks from contaminated aquifers through the deployment of suitable monitoring and mitigation measures.

Data-Driven Machine Learning Calibration Propagation in A Hybrid Sensor Network for Air Quality Monitoring

Public air quality monitoring relies on expensive monitoring stations which are highly reliable and accurate but require significant maintenance and cannot be used to form a high spatial resolution measurement grid. Recent technological advances have enabled air quality monitoring that uses low-cost sensors. Being inexpensive and mobile, with wireless transfer support, such devices represent a very promising solution for hybrid sensor networks comprising public monitoring stations supported by many low-cost devices for complementary measurements. However, low-cost sensors can be influenced by weather and degradation, and considering that a spatially dense network would include them in large numbers, logistically adept solutions for low-cost device calibration are essential. In this paper, we investigate the possibility of a data-driven machine learning calibration propagation in a hybrid sensor network consisting of One public monitoring station and ten low-cost devices equipped with NO2, PM10, relative humidity, and temperature sensors. Our proposed solution relies on calibration propagation through a network of low-cost devices where a calibrated low-cost device is used to calibrate an uncalibrated device. This method has shown an improvement of up to 0.35/0.14 for the Pearson correlation coefficient and a reduction of 6.82 µg/m3/20.56 µg/m3 for the RMSE, for NO2 and PM10, respectively, showing promise for efficient and inexpensive hybrid sensor air quality monitoring deployments.

Fixed point continuous monitoring system of oilfield skeleton and its application

Aiming at fixed monitoring well point in oil field monitoring deployment chaotic, block monitoring percentage change is big, suitable for lack of contrast data, and poor adaptability to changes in the cost problem, plane layout, well pattern deployment, give attention to two or morethings injection-production differences, etc., to determine the skeleton designated strip continuous monitoring, use the statistical methods such as cluster analysis, deep learning, combining with the field dynamic data changes, To optimize the deployment of well points, determine the principle of “skeleton well fixed continuous monitoring, nonskeleton well according to demand”, establish skeleton fixed continuous monitoring system, achieve the purpose of saving monitoring costs and improving the accuracy of reservoir evaluation. Research results show that the skeleton fixed-point continuous monitoring system to meet the needs of overall reservoir evaluation, more through rules deployment of a certain percentage of the skeleton well point and the fixed location monitoring of the stability can be compared to the continuity of the data, ensure the evaluation precision reservoir framework, at the same time using the non-framework change monitoring well balance monitoring proportion, key and the key blocks of costs change with a strong adaptability. Through research and field practice, it has been proved that the skeleton fixed-point continuous monitoring system combines oilfield monitoring deployment with traditional geological profile analysis mode, and realizes the fusion application of three system frameworks: dynamic monitoring, geological static and numerical simulation, which is helpful to promote the process of precise grid management of reservoir.

Economic evaluation of remote patient monitoring and organizational analysis according to patient involvement: a scoping review.

A literature review concerning the economic evaluation of telemonitoring was requested by the authority in charge of health evaluation in France, in a context of deployment of remote patient monitoring and identification of its financing. Due to the heterogeneity of existing telemonitoring solutions, it was necessary to stratify the evaluation according to patient involvement. Three levels of patient involvement are considered: weak (automated monitoring), medium (monitoring supported by a professional), and strong (active remote participation). We performed a scoping review to provide a comprehensive overview of different systems of telemonitoring and their reported cost-effectiveness. Following PRISMA-ScR guidelines, a search was performed in four databases: PubMed, MEDLINE, EMBASE, and Cochrane Library between January 1, 2013 and May 19, 2020. Remote patient monitoring should include the combination of three elements: a connected device, an organizational solution for data analysis and alert management, and a system allowing personalized interactions, and three degrees of involvement. We identified 61 eligible studies among the 489 records identified. Heart failure remains the pathology most represented in the studies selected (n=24). The cost-utility analysis was chosen in a preponderant way (n=41). Forty-four studies (72 percent) reported that the intervention was expected cost-effective. Heterogeneity has been observed in the remote monitoring solutions but all systems are reported cost-effective. The small number of long-term studies does not allow conclusions to be drawn on the transposability. Remote patient monitoring is reported to be cost-effective whatever the system and patient involvement.

A scalable, self‐supervised calibration and confounder removal model for opportunistic monitoring of road degradation

AbstractAssessing road degradation typically requires specialized hardware (such as laser profilometers) or labor‐intensive visual inspection. To facilitate large‐scale, timely inspection of road surfaces, opportunistic sensing is proposed: Sound and vibration measurements are obtained from vehicles that are on the road for other purposes than measuring road quality. Prior work has addressed the problem of calibration and measurement noise removal from this abundance of measurements for a small number of measurement vehicles that drive on the same roads. However, as the deployment of opportunistic monitoring progresses, the applied techniques suffer from scalability. Here, a scalable self‐supervised calibration and confounder removal (SCCR) algorithm is introduced. It allows to self‐calibrate even if the data collection is done in distinct geographic areas and is capable of generalizing to vehicles not encountered during the training phase. Several model design alternatives are explored. After the application of SCCR, supervised training on a small subset of roads allows to predict observations made by standardized techniques also in areas where the latter have not been performed. The approach is tested and validated with 41 cars driving on 23,000 km of roads.

Enablers and Barriers to Deployment of Smartphone-Based Home Vision Monitoring in Clinical Practice Settings

Telemedicine is accelerating the remote detection and monitoring of medical conditions, such as vision-threatening diseases. Meaningful deployment of smartphone apps for home vision monitoring should consider the barriers to patient uptake and engagement and address issues around digital exclusion in vulnerable patient populations. To quantify the associations between patient characteristics and clinical measures with vision monitoring app uptake and engagement. In this cohort and survey study, consecutive adult patients attending Moorfields Eye Hospital receiving intravitreal injections for retinal disease between May 2020 and February 2021 were included. Patients were offered the Home Vision Monitor (HVM) smartphone app to self-test their vision. A patient survey was conducted to capture their experience. App data, demographic characteristics, survey results, and clinical data from the electronic health record were analyzed via regression and machine learning. Associations of patient uptake, compliance, and use rate measured in odds ratios (ORs). Of 417 included patients, 236 (56.6%) were female, and the mean (SD) age was 72.8 (12.8) years. A total of 258 patients (61.9%) were active users. Uptake was negatively associated with age (OR, 0.98; 95% CI, 0.97-0.998; P = .02) and positively associated with both visual acuity in the better-seeing eye (OR, 1.02; 95% CI, 1.00-1.03; P = .01) and baseline number of intravitreal injections (OR, 1.01; 95% CI, 1.00-1.02; P = .02). Of 258 active patients, 166 (64.3%) fulfilled the definition of compliance. Compliance was associated with patients diagnosed with neovascular age-related macular degeneration (OR, 1.94; 95% CI, 1.07-3.53; P = .002), White British ethnicity (OR, 1.69; 95% CI, 0.96-3.01; P = .02), and visual acuity in the better-seeing eye at baseline (OR, 1.02; 95% CI, 1.01-1.04; P = .04). Use rate was higher with increasing levels of comfort with use of modern technologies (β = 0.031; 95% CI, 0.007-0.055; P = .02). A total of 119 patients (98.4%) found the app either easy or very easy to use, while 96 (82.1%) experienced increased reassurance from using the app. This evaluation of home vision monitoring for patients with common vision-threatening disease within a clinical practice setting revealed demographic, clinical, and patient-related factors associated with patient uptake and engagement. These insights inform targeted interventions to address risks of digital exclusion with smartphone-based medical devices.

A data-driven approach to increasing the lifetime of IoT sensor nodes

Data transmission accounts for significant energy consumption in wireless sensor networks where streaming data is generated by the sensors. This impedes their use in many settings, including livestock monitoring over large pastures (which forms our target application). We present Ambrosia, a lightweight protocol that utilizes a window-based timeseries forecasting mechanism for data reduction. Ambrosia employs a configurable error threshold to ensure that the accuracy of end applications is unaffected by the data transfer reduction. Experimental evaluations using LoRa and BLE on a real livestock monitoring deployment demonstrate 60% reduction in data transmission and a 2 times increase in battery lifetime.

Distributed acoustic sensing/surface orbital vibrator: Rotary seismic sources with fiber-optic sensing facilitates autonomous permanent reservoir monitoring

With new developments of fiber-optic sensing and rotary sources, continuous active seismic monitoring for onshore applications has now the opportunity to be fully realized and applied to enhance subsurface resource extraction and use. To date, conventional seismic monitoring deployments have primarily consisted of receiver arrays, either fixed or not, with periodic data acquisition campaigns using temporarily deployed sources, resulting in time-lapse data with poor temporal resolution. Only a few niche efforts have demonstrated continuous acquisition using fixed source-receiver networks. We have evaluated the initial results of a network of fixed rotary seismic sources, referred to as surface orbital vibrators (SOVs), coupled with a permanent distributed acoustic sensing (DAS) network at the CO2CRC Otway Field Site. Although rotary seismic sources are not new, our development of the SOV focused on simplifying the cost and complexity of the source hardware while delivering broad frequency spectrum of the source signal. The upgraded hardware is aligned with a robust methodology for autonomous operation and data processing. At the Otway Site, we deployed SOVs at nine locations, monitoring seismic response in seven DAS instrumented wells. Baseline operation of the DAS/SOV sensor array and source system demonstrates its capability with near offsets attaining a signal-to-noise ratio approaching 100 dB with a normalized root mean square of 10%. Furthermore, analyses of traveltime repeatability indicate that the DAS/SOV system can deliver time resolution of ±500 μs.