Remote Monitoring Platform Research Articles

LoRa Based IoT Platform for Remote Monitoring of Large-Scale Agriculture Farms in Chile.

Nowadays, conventional agriculture farms lack high-level automated management due to the limited number of installed sensor nodes and measuring devices. Recent progress of the Internet of Things (IoT) technologies will play an essential role in future smart farming by enabling automated operations with minimum human intervention. The main objective of this work is to design and implement a flexible IoT-based platform for remote monitoring of agriculture farms of different scales, enabling continuous data collection from various IoT devices (sensors, actuators, meteorological masts, and drones). Such data will be available for end-users to improve decision-making and for training and validating advanced prediction algorithms. Unlike related works that concentrate on specific applications or evaluate technical aspects of specific layers of the IoT stack, this work considers a versatile approach and technical aspects at four layers: farm perception layer, sensors and actuators layer, communication layer, and application layer. The proposed solutions have been designed, implemented, and assessed for remote monitoring of plants, soil, and environmental conditions based on LoRaWAN technology. Results collected through both simulation and experimental validation show that the platform can be used to obtain valuable analytics of real-time monitoring that enable decisions and actions such as, for example, controlling the irrigation system or generating alarms. The contribution of this article relies on proposing a flexible hardware and software platform oriented on monitoring agriculture farms of different scales, based on LoRaWAN technology. Even though previous work can be found using similar technologies, they focus on specific applications or evaluate technical aspects of specific layers of the IoT stack.

Assessing mechanical catheter dysfunction in automated tidal peritoneal dialysis using cycler software: a case control, proof-of-concept study

New recommendations on evaluation of peritoneal membrane function suggest ruling out catheter dysfunction when evaluating patients with low ultrafiltration capacity. We introduce the use of a combination of parameters obtained from the cycler software PD Link with HomeChoicePro (Baxter International Inc., Illinois, United States) cyclers for predicting catheter dysfunction in automated peritoneal dialysis patients (APD). Out of 117 patients treated at the Medical University of Vienna between 2015 and 2021, we retrospectively identified all patients with verified catheter dysfunction (n = 14) and compared them to controls without clinical evidence of mechanical catheter problems and a recent X-ray confirming PD catheter tip in the rectovesical/rectouterine space (n = 19). All patients had a coiled single-cuff PD catheter, performed tidal PD, and received neutral pH bicarbonate/lactate-buffered PD fluids with low-glucose degradation products on APD. Icodextrin-containing PD fluids were used for daytime dwells. We retrieved cycler data for seven days each and tested parameters' predictive capability of catheter dysfunction. Total number of alarms/week > 7 as single predictive parameter of catheter dislocation identified 85.7% (sensitivity) of patients with dislocated catheter, whereas 31.6% (1-specificity) of control patients were false positive. A combination of parameters (number of alarms/week > 7, total drain time > 22 min, ultrafiltration of last fill < 150 mL) where at least two of three parameters appeared identified the same proportion of patients with catheter dislocation, but was more accurate in identifying controls (21.1% false positive). In contrast to yearly PET measurements, an easily applicable combination of daily cycler readout parameters, also available in new APD systems connected to remote monitoring platforms shows potential for diagnosis of catheter dysfunction during routine follow-up.

Remote Monitoring for Prediction and Management of Acute Exacerbations in Chronic Obstructive Pulmonary Disease (AECOPD).

The progression of chronic obstructive pulmonary disease (COPD) is characterized by episodes of acute exacerbation (AECOPD) of symptoms, decline in respiratory function, and reduction in quality-of-life increasing morbi-mortality and often requiring hospitalization. Exacerbations can be triggered by environmental exposures, changes in lifestyle, and/or physiological and psychological factors to greater or lesser extents depending on the individual’s COPD phenotype. The prediction and early detection of an exacerbation might allow patients and physicians to better manage the acute phase. We summarize the recent scientific data on remote telemonitoring (TM) for the prediction and management of acute exacerbations in COPD patients. We discuss the components of remote monitoring platforms, including the integration of environmental monitoring data; patient reported outcomes collected via interactive Smartphone apps, with data from wearable devices that monitor physical activity, heart rate, etc.; and data from medical devices such as connected non-invasive ventilators. We consider how telemonitoring and the deluge of data it potentially generates could be combined with electronic health records to provide personalized care and multi-disease management for COPD patients.

Healthcare Professional Perspectives on the Use of Remote Patient-Monitoring Platforms during the COVID-19 Pandemic: A Cross-Sectional Study.

The COVID-19 pandemic created an urgent need to act to reduce the spread of the virus and alleviate congestion in healthcare services, protect health professionals, and help them maintain satisfactory quality and safety of care. Remote monitoring platforms (RPM) emerged as potential solutions. In this study, we evaluate, from health professionals’ perspectives, the capacity and contribution of two different digital platforms to maintain quality, safety, and patient engagement in care. A cross-sectional study was conducted using a survey in which a total of 491 health professionals participated. The results show that, in general, user perceptions of the quality and safety of care provided through the platforms were positive. The ease of access to health professionals’ services in general and shorter waiting times for patients were the two main features that were highly appreciated by most participants. However, some problems were encountered during the use of these two platforms, such as a lack of training and/or direct support for users. To improve the two platforms and maximize their use, the areas for improvement and the issues identified should be addressed as part of a collaborative process involving health professionals and patients as well as health system leaders, decision-makers, and digital platform providers.

Telemedical transport layer security based platform for cardiac arrhythmia classification using quadratic time–frequency analysis of HRV signal

The heart rate variability signal is a valuable tool for cardiovascular system diagnostics. Processing this signal detects arrhythmia during long-term cardiac monitoring. It is also analyzed to recognize abnormalities within the autonomic nervous system. Processing this signal helps in detecting various pathologies, such as atrial fibrillation (AF), supraventricular tachycardia (SVT), and congestive heart failure (CHF). As a beneficial alternative to the commonly used HRV spectrum analysis, quadratic time–frequency analysis of HRV signals could be helpful in heart pathology detection. Indeed, in this study, we have created a client-server paradigm deployed as a telemedical platform for real-time remote monitoring of the cardiovascular function in patients suffering from arrhythmia. This platform detects arrhythmia in real-time by deploying time–frequency analysis, feature extraction, feature selection, and classification of Heart Rate Variability (HRV) signals. We gathered all these functionalities in a Graphical User Interface (GUI) in addition to data acquisition. As a client, a Raspberry Pi Zero ensures data acquisition and connects to a server over TCP/IP that involves a 4G/3G connection encrypted through the transport layer security (TLS). This telemedical tool continuously monitors the heart rate variability. In the case of an alarm, medical professionals may immediately interact with their patients in the hospital or at home.

Enabling Road Detection Tasks via Collaborative Smart Vehicles

Vehicular cloud computing (VCC) has been widely utilized to enhance traffic management and road safety both in the static and dynamic scenarios. In VCC, the onboard resources of involved smart vehicles (SVs) would be integrated as a resource pool and collaboratively accommodate various computation tasks. In this system, SVs in a section of the road can be grouped as a vehicular cloud to complete the detection job, e.g., collecting and preprocessing the road data. Then, an SV will be selected to aggregate all SVs’ detection results in the road section and upload the results to the remote monitoring platform via base stations (BSs). However, due to the high dynamics of vehicular mobility and the poor coverage of BSs in some areas, this vision is challenging to achieve. In this article, we propose a collaborative road detection system and schedule the detection task to different SVs in the road section. Specifically, 1) we model the collaborative detection process and formulate it as a task scheduling problem to minimize the response time, which is NP-hard. An adaptive location-aware scheduling scheme is proposed for task scheduling and 2) as there are road sections without coverage of any BS, therefore we have to utilize the SVs on the opposite lane to transmit the aggregation result in these road sections. Accordingly, an uploading strategy is proposed to decide to upload the feedback through SVs on the opposite lane or in the coverage area of the next BS. Extensive experiments show that our scheme can significantly reduce the response time overall and is close to the optimal solution.

Adaptive IoT Technology for Measuring Salinity, Dissolved Oxygen, and pH in Aquatic Environments

This paper presents an extension to an IoT platform for remote near real-time aquatic environmental monitoring that incorporates electrical conductivity (i.e., salinity), dissolved oxygen and potential of hydrogen (pH) sensors. The predecessor to this system could be remotely deployed for extended periods of time, but was limited to measuring temperature, lux (light) and turbidity only. This paper outlines how the platform was expanded upon to include the additional environmental parameters (i.e., salinity, dissolved oxygen and pH) by selecting the appropriate compatible sensor technologies, redesigning the electronic componentry/physical buoy, and undertaking thorough system integration testing. We present the hardware and software challenges faced to adapt the platform to the new sensor parameters, illustrate the latest buoy design, describe the calibration process and demonstrate in-house and commercial field-testing. The system can be deployed for 12 months between maintenance cycles and has been used in environmental research and commercial prawn farm water quality monitoring.

HRV Monitoring Using Commercial Wearable Devices as a Health Indicator for Older Persons during the Pandemic.

Remote monitoring platforms based on advanced health sensors have the potential to become important tools during the COVID-19 pandemic, supporting the reduction in risks for affected populations such as the elderly. Current commercially available wearable devices still have limitations to deal with heart rate variability (HRV), an important health indicator of human aging. This study analyzes the role of a remote monitoring system designed to support health services to older people during the complete course of the COVID-19 pandemic in Brazil, since its beginning in Brazil in March 2020 until November 2021, based on HRV. Using different levels of analysis and data, we validated HRV parameters by comparing them with reference sensors and tools in HRV measurements. We compared the results obtained for the cardiac modulation data in time domain using samples of 10 elderly people’s HRV data from Fitbit Inspire HR with the results provided by Kubios for the same population using a cardiac belt, with the data divided into train and test, where 75% of the data were used for training the models, with the remaining 25% as a test set for evaluating the final performance of the models. The results show that there is very little difference between the results obtained by the remote monitoring system compared with Kubios, indicating that the data obtained from these devices might provide accurate results in evaluating HRV in comparison with gold standard devices. We conclude that the application of the methods and techniques used and reported in this study are useful for the creation and validation of HRV indicators in time series obtained by means of wearable devices based on photoplethysmography sensors; therefore, they can be incorporated into remote monitoring processes as seen during the pandemic.

Remote monitoring of COVID–19 patients using home pulse oximetry and virtual platform: an observational study

Background: The coronavirus disease (COVID–19) is a global health problem with ever-increasing attributed deaths. Patients with COVID–19 may deteriorate rapidly, and their continuous monitoring is necessary. Monitoring and providing care beyond the traditional face-to-face model is a new chapter in the history of medicine. It consists of remote monitoring and management using virtual platform as a critical component in the ever-changing environment of this deadly pandemic. We evaluated the impact of remote monitoring of the patients at their residences after being infected with SARS-COV-2 virtually.&#x0D; Methodology: This observational study was conducted among patients with symptoms of COVID–19 attending in the Fever Clinic between January and March 2021. The study was set to continue for the duration of the pandemic. Access to pulse oximeter and virtual platform was given to the enrolled patients. The participants were monitored in a remote monitoring platform at home and during the hospital stay. The primary outcome measures included number of recovery i.e. discharged from remote home monitoring and/or hospital and escalation of management such as hospitalization or transfer to the ICU facilities. Duration of hospital stay, patient experience and satisfaction, and barriers to use the virtual platform were included in the secondary outcome measures.&#x0D; Results: A total of twenty patients (median age 45 y, male to female ratio 65:35) were included. The median duration of monitoring was 10 days (IQR 8-12 days). Out of twenty patients, 3 (15%) patients needed hospitalization and were recognized in time by the team. The duration of hospital stay was 4 days median (IQR 3-6 days). All patients were discharged from this service safely; no death was reported. Ninety percent of the studied population were satisfied and felt reassured by this remote monitoring service.&#x0D; Conclusion: Early recognition of deterioration and prompt management through remote monitoring model increases the chances of survival of patients, and reduces the morbidity. Such remote monitoring model can be applied for future outbreaks of pandemics.&#x0D; Key words: COVID–19; Remote monitoring; Pulse oximetry; Emerging technologies; Virtual platform; Likert’s scale; Satisfaction.&#x0D; Citation: Akhtaruzzaman AKM, Kamal MM, Parveen M, Rabbi M, Dhali R, Islam MS, Bhowmick DK Remote monitoring of COVID–19 patient using home pulse oximetry and virtual platform: an observational study. Anaesth. pain intensive care 2022;26(1):89-95.&#x0D; DOI: 10.35975/apic.v26i1.1773&#x0D; Received: December 14, 2021, Reviewed: January 16, 2022, Accepted: January 22, 2022

P665 Towards FAIR longitudinal IBD registries: preliminary results of an iterative design process of a reusable and interoperable IBD database model

Abstract Background The Real World Data [RWD] of the Dutch longitudinal IBD registries (population based IBD South Limburg cohort, Parelsnoer IBD, Initiative on Crohn and Colitis drug-registry) have proven to answer knowledge gaps regarding epidemiology, patient stratification, positioning and effectiveness and safety of new drugs. Life-long data-capture in registries for large cohorts is extremely labor intensive and expensive. Therefore, based on the database models of existing registries, we aim to develop a reusable and interoperable Real World IBD database model that will contribute re-use of every-day care data form hospital information systems and remote monitoring platforms. This project contributes to generation of high quality, multi-dimensional data for (inter)national IBD registries such as the United Registries for Clinical Assessment and Research [UR-CARE] registry and aforementioned registries. Methods The design process was iterative and involved the potential end-users of the registries. For the first iteration the database models of established Dutch IBD cohorts were taken as input, in combination with repeated discussions on the content with three principal IBD investigators. The second iteration was multifaceted including; 1) Five discussion sessions with 7 IBD researchers (MD medicine of which the majority practiced IBD care and were experienced with the hospital information system) discussing the content, and 2) four discussion sessions with researchers experienced in re-using hospital care data for research that evaluated the model on practical use and difficulties in data interpretation using the output of the first iteration. No data was analyzed, only codebooks describing variables and their values. Results The first iteration of the design phase resulted in a database model called IBD common data model [IBDCDM] v1 consisting out of 395 variables, and based on existing codebooks of Dutch IBD registries (figure 1). The database model, called IBD common data model [IBDCDM] v1 consists out of 27 independent modules on different themes important for the IBD disease course allowing for repeated data recordings (figure 2). The second iteration of the design phase resulted in pruning the modules’ content, removal of one module, merging and adding of modules. Conclusion An iterative design process involving end-users will result in higher data quality by optimizing practical usability and interpretability of the database model. Next step is to encode the model using medical standardized vocabularies (e.g. SNOMED, LOINC), for interoperability. The database model contributes towards producing reusable IBD datasets that use study hospital care data as a primary source to study the IBD disease course over several decades.

DOP07 Chronic abdominal pain in IBD patients in remission: Real-world data on contributing factors

Abstract Background Chronic abdominal pain is highly prevalent in IBD patients in remission. The aetiology is incompletely understood, although persistent histologic inflammation, post-inflammatory visceral hypersensitivity, and altered gut-brain interaction are believed to contribute. Data on the characteristics of IBD patients suffering from chronic abdominal pain are sparse, yet essential for the identification of treatment targets. We investigated clinical, lifestyle and psychosocial factors associated with chronic abdominal pain in a real-world cohort of IBD patients in remission. Methods A prospective multicentre study was performed enrolling consecutive IBD patients, between Jan 1, 2020 and Jul 1, 2021, using myIBDcoach, an established remote monitoring platform for IBD. Patient reported outcome measures on disease activity, lifestyle and psychosocial factors (i.e. depressive symptoms, anxiety, stress, and life events) were assessed in three-monthly intervals. Chronic abdominal pain in IBD in remission (IBDremissionPain+) was defined as an abdominal pain score ≥3 (1–10 numeric rating scale (NRS)) at ≥1/3 of all assessments combined with faecal calprotectin &amp;lt;150 μg/g in 90 days around periodic assessments. Multivariable logistic regression, adjusting for relevant confounders, was performed to identify risk factors for IBDremissionPain+ compared to patients in remission without chronic abdominal pain (IBDremissionPain-). Results In total, 559 patients were followed prospectively, of which 429 (76.7%) were in biochemical remission. Of these, 198 (46.2%) fulfilled the criteria for chronic abdominal pain. IBDremissionPain+ patients were characterized by female sex, higher BMI, and shorter disease duration compared to IBDremissionPain- (Table 1). IBDremissionPain+ patients reported significantly higher levels of stress, fatigue, depressive and anxiety symptoms, and occurrence of life events (Table 2). On multivariable logistic regression, female sex (aOR 2.58), shorter disease duration (&amp;lt;10years, aOR 2.31), higher BMI (aOR 1.06), higher levels of stress (aOR 1.19), fatigue (aOR 4.73), and life events (aOR 1.65) were all significantly associated with chronic abdominal pain (Table 3). The univariable association between pain and anxiety and depressive symptoms was modulated by stress in the multivariable analysis. Conclusion In this real-world population of IBD patients in remission, 46.2% experience chronic abdominal pain, characterized by female sex, shorter disease duration, higher BMI, fatigue and psychosocial factors. The gut-brain interaction in this population is represented by higher levels of depressive and anxiety symptoms, but the relation to abdominal pain is potentially modulated through increased levels of perceived stress.

Implantable Cardioverter Defibrillator Multisensor Monitoring during Home Confinement Caused by the COVID-19 Pandemic.

Simple SummaryDuring the COVID-19 pandemic, utilization of remote monitoring platforms was recommended. The HeartLogic algorithm identifies patients at risk of heart failure events, combining multiple sensors available on implantable cardioverter defibrillators. This analysis examined how multiple CIED sensors behave in periods of anticipated restrictions pertaining to physical activity. We demonstrated a significant drop in median activity level immediately after the implementation of stay-at-home orders, whereas there was no difference in the other contributing sensors. The weekly rate of heart failure alerts was significantly higher during the lockdown and post-lockdown than that reported in the pre-lockdown. Aims: The utilization of remote monitoring platforms was recommended amidst the COVID-19 pandemic. The HeartLogic index combines multiple implantable cardioverter defibrillator (ICD) sensors and has proved to be a predictor of impending heart failure (HF) decompensation. We examined how multiple ICD sensors behave in the periods of anticipated restrictions pertaining to physical activity. Methods: The HeartLogic feature was active in 349 ICD and cardiac resynchronization therapy ICD patients at 20 Italian centers. The period from 1 January to 19 July 2020, was divided into three phases: pre-lockdown (weeks 1–11), lockdown (weeks 12–20), post-lockdown (weeks 21–29). Results: Immediately after the implementation of stay-at-home orders (week 12), we observed a significant drop in median activity level whereas there was no difference in the other contributing parameters. The median composite HeartLogic index increased at the end of the Lockdown. The weekly rate of alerts was significantly higher during the lockdown (1.56 alerts/week/100 pts, 95%CI: 1.15–2.06; IRR = 1.71, p = 0.014) and post-lockdown (1.37 alerts/week/100 pts, 95%CI: 0.99–1.84; IRR = 1.50, p = 0.072) than that reported in pre-lockdown (0.91 alerts/week/100 pts, 95%CI: 0.64–1.27). However, the median duration of alert state and the maximum index value did not change among phases, as well as the proportion of alerts followed by clinical actions at the centers and the proportion of alerts fully managed remotely. Conclusions: During the lockdown, the system detected a significant drop in the median activity level and generated a higher rate of alerts suggestive of worsening of the HF status.

Design of AGV Fault Diagnosis Data Acquisition System Based on Cloud Platform

With the development of industrial Internet of Things technology, online fault diagnosis system has become an important part of remote operation and maintenance. Aiming at the problem that the AGV online fault diagnosis system requires higher real-time data, more data volume and faster transmission rate, this paper proposes a cloud platform-based AGV fault diagnosis data acquisition system. The system uses the STM32 single-chip microcomputer to collect the sensor data carried by the AGV, uses the wireless WIFI module to upload the data to the cloud server, and finally saves the data to the cloud database after conversion and verification, which provides a test platform for the Web remote real-time monitoring and online fault diagnosis system. By building an AGV test platform to conduct experiments, the results show that the throughput rate of the system is 90PPS, and the packet loss rate is within 0.1%. The system can meet the data requirements of the online fault diagnosis system, and has good real-time performance and stability.

Grow-IoT (smart analytics app for comprehensive plant health analysis and remote farm monitoring using smart sensors)

Agriculture is the primary source of livelihood for a large section of the society in India, and the ever-increasing demand for high quality and high quantity yield calls for highly efficient and effective farming methods. Grow-IoT is a smart analytics app for comprehensive plant health analysis and remote farm monitoring platform to ensure that the farmer is aware of all the critical factors affecting the farm status. The cameras installed on the field facilitate capturing images of the plants to determine plant health based on phenotypic characteristics. Visual feedback is provided by the computer vision algorithm using image segmentation to classify plant health into three distinct categories. The sensors installed on the field relay crucial information to the Cloud for real-time optimized farm status management. All the data relayed can then be viewed using the user-friendly Grow-IoT app to remotely monitor integral aspects of the farm and take the required actions in case of critical conditions. Thus, the mobile platform combined with computer vision for plant health analysis and smart sensor modules gives the farmer a technical perspective. The simplistic design of the application makes sure that the user has the least cognitive load while using it. Overall, the smart module is a significant technical step to facilitate efficient produce across all seasons in a year.

Design of a Multi-Sensors Wearable Platform for Remote Monitoring of Knee Rehabilitation

Smart wearables are a promising tool for the objective and quantifiable monitoring of patients’ capabilities during remote at-home assessments. A novel platform for the remote assessment of patients undergoing knee rehabilitation has been presented in this paper, SKYRE. The challenges associated with the design of the SKYRE platform are described. The platform consists of a multi-sensor wearable garment and an associated ICT architecture, with the aim of capturing real-time objective assessment of physical rehabilitation exercises and support clinicians in their decision-making process as well as provide guidance to the end-users so as to increase their awareness and compliance. The overall system architecture is defined based on users’ requirements and industrial design, and both hardware and software platforms have been thoroughly discussed in detail, including electronic design, textile integration, prototyping process, and firmware development, as well as the mobile application and web portal implementation. Multiple sensing technologies are adopted, including motion capture, electromyography measurements, and muscle electro-stimulation. The developed system, SKYRE, meets the end-users’ requirements, and the validation shows that the system presents results comparable to gold-standard technologies. SKYRE therefore might represent a valid alternative for patients and clinicians willing to perform a remote objective assessment of the rehabilitation process following knee surgery.

JTrack: A Digital Biomarker Platform for Remote Monitoring of Daily-Life Behaviour in Health and Disease.

Health-related data being collected by smartphones offer a promising complementary approach to in-clinic assessments. Despite recent contributions, the trade-off between privacy, optimization, stability and research-grade data quality is not well met by existing platforms. Here we introduce the JTrack platform as a secure, reliable and extendable open-source solution for remote monitoring in daily-life and digital-phenotyping. JTrack is an open-source (released under open-source Apache 2.0 licenses) platform for remote assessment of digital biomarkers (DB) in neurological, psychiatric and other indications. JTrack is developed and maintained to comply with security, privacy and the General Data Protection Regulation (GDPR) requirements. A wide range of anonymized measurements from motion-sensors, social and physical activities and geolocation information can be collected in either active or passive modes by using JTrack Android-based smartphone application. JTrack also provides an online study management dashboard to monitor data collection across studies. To facilitate scaling, reproducibility, data management and sharing we integrated DataLad as a data management infrastructure. Smartphone-based Digital Biomarker data may provide valuable insight into daily-life behaviour in health and disease. As illustrated using sample data, JTrack provides as an easy and reliable open-source solution for collection of such information.

Remote Patient Monitoring Program for COVID-19 Patients Following Hospital Discharge: A Cross-Sectional Study

Background: The COVID-19 pandemic created an urgent need to act to reduce the spread of the virus and alleviate congestion from healthcare services, protect healthcare providers, and help them maintain satisfactory quality and safety of care. Remote COVID-19 monitoring platforms emerged as potential solutions.Objective: The purpose of this study was to evaluate the capacity and contribution of two different platforms used to remotely monitor patients with COVID-19 to maintain quality, safety, and patient engagement in care, as well as their acceptability, usefulness, and user-friendliness from the user's perspective. The first platform is focused on telecare phone calls (Telecare-Covid), and the second is a telemonitoring app (CareSimple-Covid).Methods: We performed a cross-sectional study. The data were collected through a phone survey from May to August 2020. Data were analyzed using descriptive statistics and t-test analysis. Participants' responses and comments on open-ended questions were analyzed using content analysis to identify certain issues and challenges and potential avenues for improving the platforms.Results: Fifty one patients participated in the study. Eighteen participants used the CareSimple-Covid platform and 33 participants used the Telecare-Covid platform. Overall, the satisfaction rate for quality and safety of care for the two platforms was 80%. Over 88% of the users on each platform considered the platforms' services to be engaging, useful, user-friendly, and appropriate to their needs. The survey identified a few significant differences in users' perceptions of each platform: empathy toward users and the quality and safety of the care received were rated significantly higher on the CareSimple-Covid platform than on the Telecare-Covid platform. Users appreciated four aspects of these telehealth approaches: (1) the ease of access to services and the availability of care team members; (2) the user-friendliness of the platforms; (3) the continuity of care provided, and (4) the wide range of services delivered. Users identified some technical limitations and raised certain issues, such as the importance of maintaining human contact, data security, and confidentiality. Improvement suggestions include promoting access to connected devices; enhancing communications between institutions, healthcare users, and the public on confidentiality and personal data protection standards; and integrating a participatory approach to telehealth platform development and deployment efforts.Conclusion: This study provides preliminary evidence that the two remote monitoring platforms are well-received by users, with very few significant differences between them concerning users' experiences and views. This type of program could be considered for use in a post-pandemic era and for other post-hospitalization clienteles. To maximize efficiency, the areas for improvement and the issues identified should be addressed with a patient-centered approach.

A Sensor-Based mHealth Platform for Remote Monitoring and Intervention of Frailty Patients at Home.

Frailty syndrome is an independent risk factor for serious health episodes, disability, hospitalization, falls, loss of mobility, and cardiovascular disease. Its high reversibility demands personalized interventions among which exercise programs are highly efficient to contribute to its delay. Information technology-based solutions to support frailty have been recently approached, but most of them are focused on assessment and not on intervention. This paper describes a sensor-based mHealth platform integrated in a service-based architecture inside the FRAIL project towards the remote monitoring and intervention of pre-frail and frail patients at home. The aim of this platform is constituting an efficient and scalable system for reducing both the impact of aging and the advance of frailty syndrome. Among the results of this work are: (1) the development of elderly-focused sensors and platform; (2) a technical validation process of the sensor devices and the mHealth platform with young adults; and (3) an assessment of usability and acceptability of the devices with a set of pre-frail and frail patients. After the promising results obtained, future steps of this work involve performing a clinical validation in order to quantify the impact of the platform on health outcomes of frail patients.

RETRACTED ARTICLE: Research on data transmission model of agricultural wireless sensor network based on game theory

This article has been retracted from publication in the Taylor & Francis journal, Acta Agriculturae Scandinavica, Section B — Soil & Plant Science. Following publication, concerns were raised by multiple third-parties around the content of the special issue and the decision-making process. Following an investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editor-in-Chief, it was confirmed that this article included in Special Issue titled “ Envisage Computer Modelling and Statistics for Agriculture”, guest edited by Gunasekaran Manogaran was not peer-reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines.

Internet of Things and Intelligent Transportation System

As the core of the new monitoring system, the Internet of Things realizes the integration of wireless sensor networks and traditional communication networks, and provides a platform for remote management and monitoring of the underlying equipment. The intelligent transportation system framework built on this basis combines intelligent transportation technology and the organic combination of vehicle management technologies is conducive to the safety, speed and reliability of vehicle transportation, and plays an important role in further reducing transportation costs. Based on this, this article launched the research on the Internet of Things and intelligent transportation systems. This article first summarizes the concepts of the Internet of Things, intelligent transportation and wireless sensor network technology and the current research status at home and abroad. By analyzing and comparing the performance and characteristics of various communication methods, embedded core microprocessors and embedded operating systems, this paper proposes an overall design scheme of the Internet of Things transportation system based on embedded technology. This paper analyzes the path planning problems in the application of the transportation system, combining the shortest path algorithm simulation results and the actual characteristics of the transportation network, and proposes a simulation data fitting method based on two network parameters and Bellman-Ford, Dijkstra, and Floyd algorithms. The route optimization scheme, and the above-mentioned design scheme was implemented in the transportation system, and the scheme verification was carried out. Finally, this article describes in detail the overall debugging process and operating results of the transportation system, thereby fully verifying the feasibility and correctness of the design and implementation methods of the intelligent transportation system based on the Internet of Things. The research results show that when the INF-PROPORTION is small, the Dijkstra algorithm is better than the Bellman-Ford algorithm. When INF-PROPORTION=0.3, the two algorithms T overlap. Since then, the advantages of the Bellman-Ford algorithm gradually appear, but it is approaching in INF-PROPORTION. At 1 o’clock, the Dijkstra algorithm has a sharp decrease, which is again smaller than the Bellman-Ford algorithm. The second loop condition in the main loop of Dijkstra’s algorithm cannot be satisfied, resulting in a decrease in T.