Remote Patient Monitoring System Research Articles

ACGAN for Addressing the Security Challenges in IoT-Based Healthcare System

The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people’s lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets—biometric and network flow metric—have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results.

Actual problems of remote patient monitoring

Remote patient monitoring (RPM) is an innovative technology that provides continuous monitoring of patients' health from a distance. The RPM system is versatile because it can be used in the treatment of acute and chronic conditions, helping doctors keep their patients under control between ward rounds or the absence of personal care. However, the implementation of this system is accompanied by numerous problems: from technical to hospital staff management problems. The object of this study is the technical challenges associated with the use of the RPM system. The problem lies in the imperfection of many elements of RPM, including: data management, sensor accuracy, compatibility and user acceptance of the technology. The research found that these technical challenges arise from insufficient standardization, poor sensor data quality, and interoperability issues between medical platforms. These results are explained by the complexity of the technological infrastructure and the need to improve the qualifications of medical workers. The main goal of the current article is to solve the scientific and technical problem of RPM through the proposal of an effective practical development - an automated system for remote monitoring of patients. Since most processes are moving towards their automation, by using the latest scientific developments, among which we can recall machine learning and neural networks and Internet of Things (IoT) technology, it was decided to consider the factors of their application in remote patient monitoring systems. The results of this study can be used to optimize RPM systems in hospitals, as well as to improve the reliability and efficiency of new automated monitoring systems, which is extremely important in the context of war, which causes a shortage of medical personnel and an increase in the number of potential patients per doctor.

The effect and implementation of the COVID Box, a remote patient monitoring system for patients with a COVID-19 infection in primary care: a matched cohort study

The effect and implementation of the COVID Box, a remote patient monitoring system for patients with a COVID-19 infection in primary care: a matched cohort study

A Remote Patient Monitoring System with Feedback Mechanisms using a Smartwatch: Concept, Implementation and Evaluation based on the activeDCM Randomized Controlled Trial.

Technological advances allow recording and sharing of health-related data in a patient-centric way using smartphones and wearables. Secure sharing of such patient-generated data with physicians would enable a dense management of individual health trajectories, monitoring of risk factors and asynchronous feedback. However, most Remote Patient Monitoring (RPM) systems currently available are not fully integrated into hospital IT systems or lack the patient-centric design. The objective was to conceptualize and implement a user-friendly, reusable, interoperable and secure RPM system incorporating asynchronous feedback mechanisms, using a broadly available consumer wearable (Apple Watch). Additionally, the study sought to evaluate factors influencing patient acceptance of such systems. The RPM system requirements were established through focus group sessions. Subsequently, a system concept was designed and implemented using an iterative approach, ensuring technical feasibility from the beginning. To assess clinical feasibility, the system was employed as part of the activeDCM prospective, randomized, interventional study focusing on Dilated Cardiomyopathy (DCM). Each patient used the system for at least 12 months. The System Usability Scale (SUS) was employed to measure usability from a subjective patient perspective. Additionally, an evaluation was conducted on the objective wearable interaction frequency as well as the completeness of transmitted data, classified into Sensor-based Health Data (SHD) and Patient Reported Outcome Measures (PROM). Descriptive statistics using boxplots, along bootstrapped multiple linear regression with a 95% confidence interval (CI) were utilized for evaluation, analyzing the influence of age, sex, device experience and intervention group membership. The RPM system consists of four interoperable components: patient-devices, data-server, data-viewer and notification-service. The evaluation of the system was conducted with 95 consecutive DCM patients (female: 28 of 95 (29%), age: 50±12 years) completing the activeDCM study protocol. The wearable/ smartphone application of the system achieved a mean SUS score of 78±17, which was most influenced by device experience. 83 of 95 patients (87%) could integrate the wearable application (very) well into their daily routine and 67 of 95 (70%) saw a benefit of the RPM system for management of their health condition. Patients interacted on average with the wearable on 61%±26% of days enrolled in the study, corresponding to 239±99 of 396±39 days. SHD was available on average for 78%±23% of days and PROM data 64%±27% of weeks enrolled in the study, corresponding to 307±87 of 396±39 days and 35±15 of 56±5 weeks, respectively. Wearable interaction frequency, SHD and PROM completeness were most influenced by intervention group membership. Our results mark a first step towards integrating RPM systems, based on a consumer wearable device for primary patient input, into standardized clinical workflows. They can serve as a blueprint for creating a user-friendly, reusable, interoperable and secure RPM system, that can be integrated into patients' daily routines. ClinicalTrials.gov-Identifier: NCT04359238.

A Novel Design and Performance Assessment of a Blockchain-Powered Remote Patient Monitoring System

A Novel Design and Performance Assessment of a Blockchain-Powered Remote Patient Monitoring System

Security Risk Assessment of IoT for Remote Patient Monitoring System

Remote Patient Monitoring (RPM), an essential component of telehealth, is among the biggest changes marked in healthcare. It facilitates patients’ treatment, remotely at home or in some distant location, without traditional clinical settings. The RPM plays a key role in data acquisition, data analysis and insights, and improved healthcare management. It collects real-time data using wearable sensors and mobile apps, and furnish crucial health metrics. Advanced algorithms and prognostic modelling than process the data, pattern and likely health risks to predict any disease more precisely and accurately for early action. RPM provides real-time watch and remote consultation that help in improved disease control and better patients’ care. Improved accuracy, reduced cost, real time interaction, and refined patient well-being are the significant healthcare benefits of RPM. Prognosis Health & Management (PHM) system is used for predicting the remaining useful life (RUL) of healthcare assets such as sensors, pacemakers, defibrillators and other medical equipment. The PHM leads to pre-emptive maintenance, lowers downtime, and tracks the life span of such healthcare equipment. The IoT enables PHM to monitor remote assets and gather instant data to foresee the RUL of such equipment. Providing the facilitations, PHM also creates potential vulnerabilities of exposure of device, network and data, and poses data security and privacy challenges. Therefore, strong security controls are required to keep patients’ data confidential and safe from uneven approaches to technology and data breaches. This paper evaluates risk associated with RPM using OCTAVE ALLGERO, a risk assessment framework, to mitigate the data security concerns.

IoT-Based Heartbeat Rate-Monitoring Device Powered by Harvested Kinetic Energy.

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical damping is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method.

Impact of Remote Patient Monitoring Systems on Nursing Time, Healthcare Providers, and Patient Satisfaction in General Wards.

Remote patient monitoring systems (RPMS) are increasingly integrated into hospital wards to improve patient safety and reduce the workload on healthcare professionals (HCPs). This study evaluates the efficacy of RPMS in general wards, focusing on their impact on nursing efficiency, patient care, HCPs, and patient satisfaction. A comprehensive time-motion study was conducted along with surveys targeting HCPs and patients in M.S. Ramaiah Memorial Hospital, Bangalore, India, which has implemented RPMS in general wards. The study involved observing and comparing nursing activities in RPMS-equipped wards versus control wards without RPMS across various shifts. In addition, feedback on the system's impact on patient safety, overall care quality, and usability was gathered through a survey form. RPMS decreases the amount of time nurses spend on routine monitoring, communication, and coordination, enabling a 43.11% increase in time available for patient care. More than 89% ofHCPs noted improvements in the level of care and overall patient safety. More than 80% of the HCPs also noted improvement in the patient's experience. More than 50% of HCPs find RPMS easy to use and user-friendly.More than 60% of the patients noted an overall improvement in care quality. RPMS has proven to be a valuable asset in hospital wards, enhancing patient monitoring and safety while reducing the workload on staff. In addition, significant time savings on routine tasks and high satisfaction levels from both staff and patients underscore the system's benefits.

Telemedicine for the Underserved Racial and Ethnic Minorities During COVID-19 and Beyond.

Objective: To demonstrate that a culturally and linguistically appropriate telehealth protocol can be implemented to improve the glycemic control of patients as an extension of regular clinical services and provide continuity of care. Methods: A telehealth platform was established during COVID-19 pandemic and from numerous telehealth encounters we sampled 498 patients who received telehealth intervention over a 12-month period for specific services: Rx refill, consultation for laboratory results, wellness evaluation and education, and acute or sick visits with appropriate referrals. This telehealth platform was integrated with a remote patient monitoring system utilizing a Bluetooth-enabled glucometer for patients with diabetes compared to their abnormal baseline hemoglobin A1C (HgA1C). The Blood sugar values were recorded at predefined intervals to monitor controls for diabetes. The ethnic diversity and level of education of patients required addressing the digital divide, language interpretation, and navigation at each monitoring step. Results: This method demonstrated that a culturally and linguistically appropriate telehealth protocol can be implemented to improve the glycemic control of patients in an intervention group compared with a control group. Validation of the glycemic control was based on 70 patients identified as eligible for participation based on the inclusion criteria: a HgA1C level of 7% or higher obtained within the last 10 months. Informed consent was obtained for 42 participants based on patient participation constraints during the COVID-19 pandemic. Conclusions: We conclude that telemedicine procedures utilized for patients with little or no prior knowledge of remote self-monitoring methods can support their treatment of chronic diseases, such as diabetes. The outcomes from the implementation of telemedicine services were observed in a well-defined group of underserved racial and ethnic minority patients at our clinic. We now have a protocol to expand this to other chronic diseases and used as a regular clinical procedure.

Delay of Transmitted Data in the Remote Patient Monitoring System through AMQP and CoAP

Remote Patient Monitoring (RPM) is a healthcare solution that uses technology to monitor patients outside conventional healthcare settings. It is especially useful for people with chronic conditions or needing regular monitoring. One of the main reasons for the increase in the number of deaths each year is the increase in cardiovascular diseases, including hypertension. Online blood pressure monitoring offers many advantages but also potential challenges. This work reviews the key communication technologies and research challenges in the real-time transmission of measured blood pressure data. Delay in these systems is not tolerated as it involves human lives. To conduct the experimental studies, a prototype of an experimental intelligent system was created to study the delay and processor load of the RPI4 gateway. The measured blood pressure data is sent to the Things Board cloud using the AMQP and CoAP protocols. The experimental results are particularly useful for RPM system designers. The results of this research facilitate an informed decision on the choice of protocol that transmits the data from the gateway to the cloud in the process of designing remote patient monitoring systems.

Abstract PO1-10-10: Leveraging technology to optimize symptom management and patient empowerment in routine breast cancer care: implementation of a remote patient monitoring (RPM) pathway across 24 hospitals in Europe

Abstract Background: In clinical trials, RPM has been proven to improve the quality of life and toxicity management for patients receiving systemic treatment and is recommended by international guidelines for routine clinical practice (Di Maio et al., Ann Oncol 2022). Resilience is an oncology-specific RPM system (CE marked, class IIa medical device - mobile application [app] or web interface) which prompts patients to complete a weekly survey including core symptoms from NCI’s PRO-CTCAE questionnaire. Severe or worsening symptoms trigger an alert notification sent to the patient’s care team. In addition to the RPM system, the mobile app offers a large pool of breast cancer-specific educational, self-management, and patient empowerment resources that are personalized according to symptoms reported, disease stage, and treatments received. Here we present the implementation of the Resilience RPM pathway in routine breast cancer care across 24 highly diverse cancer centers. Methods: The implementation process followed a phasic process of Exploration, Preparation, Implementation, and Sustainability that involved close collaboration with technology providers, hospital managers, and care teams at participating cancer centers to coordinate, train, provide technical assistance, and respond to site-specific needs. In addition, monthly webinars offered participating centers an opportunity to share best practices and ways to overcome implementation barriers. A dedicated platform with educational RPM resources for healthcare providers was also available. A process evaluation according to the RE-AIM framework was performed assessing the reach, adoption, implementation, and maintenance of the RPM pathway in routine care. The effectiveness of the pathway was assessed as the ability of the care team to timely respond to alerts. Aggregated system level metric of RPM was collected covering patients with breast cancer under active systemic treatment followed by Resilience from 01-11-2021 to 29-06-2023. Usage data of mobile app resources was tracked from 01-01-2023 to 29-06-2023. Qualitative assessments with focus groups complemented this evaluation. Results: 985 patients, 1% male, 99% female, with a median age of 57y (range: 21-95y; P25-75 38.5-64.5y), were registered across 24 cancer centers. In the last 6 months, the predominant RPM interface chosen by patients was the mobile app (64.1 vs 35.9% for the web platform). Adherence to weekly ePRO reporting was 89.4% in the mobile app and 86.6% in the web platform. The median time from prompt receipt to survey completion was 5.6 hours. The median time for the nurse navigator (NN) to process an alert was 3h50min. Most alerts (91%) were handled directly by NN with 9% requiring the support of a physician. The mean number of individual content consumed per patient was 13 (6744 resources/523 pts, 100% of app users accessed resources). Preferred formats of content delivery were written articles (66.5%), followed by videos (23.1%) and podcasts (10.4%). Most assessed contents referred to: symptom management advice (16%), followed by information about breast cancer disease characteristics (12.5%), supportive care strategies (11.5%), self-guided exercises of mindfulness-based stress reduction (9.4%), and breast cancer treatment journey (8.3%). Qualitative assessments of the app interface, educational content, and self-management programs revealed high levels of satisfaction. The RPM pathway is sustained in the 24 cancer centers and implementation is expanding to additional centers. Conclusion: The Resilience RPM pathway was successfully implemented in routine care across a diverse group of cancer centers with elevated levels of patient engagement and responsiveness from NN. In addition, patients accessed evidence-based educational and empowerment resources, adapted to their medical profile according to their preferences and learning style. Citation Format: Maria Alice Franzoi, Arlindo R Ferreira, Juliette Fanton d'Andon, Thomas Grellety, Eliane Ithurbide-Dachary, Laura Polastro, Laurence Caravella, Joseph Rodriguez, Nathanaelle Leprevier, Elsa Blanchard, Stéphane Remy, Belen d' Ythurbide, Romain Rivoirard, Raoudha Boughzala, Joana M Ribeiro, Ines Vaz Luis. Leveraging technology to optimize symptom management and patient empowerment in routine breast cancer care: implementation of a remote patient monitoring (RPM) pathway across 24 hospitals in Europe [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-10-10.

IoT-Based Virtual Primary Clinic: Remote Patient Monitoring and Consultation System

Using Internet of Things technology, the Virtual Primary Clinic project is a cutting-edge healthcare endeavor that enables remote patient monitoring and consultation. The project collects essential health metrics including body temperature, oxygen saturation, heartbeat, and ECG signals by integrating a variety of sensors, including NodeMCU ESP32, SpO2, DHT11, and AD8232 ECG. The information collected by these sensors is sent via API to a specially created website and an online platform called ThingSpeak. With features including patient registration, appointment scheduling, access to medical histories, and doctor-patient contact, the website acts as a comprehensive interface between patients and physicians. Patients can register on the website, provide their medical information, and get medications sent to them remotely based on their conditions. However, without having to see patients in person, doctors can examine patient data, diagnose ailments, and write prescriptions or offer medical advice.The Virtual Primary Clinic project intends to improve healthcare accessible, especially in remote or underserved locations, by enabling remote monitoring and consultation. In addition to providing healthcare providers with an effective platform for remote patient care, it gives patients the ability to take charge of their health. This project is a major step toward using cutting-edge IoT-based technologies to improve patient outcomes and democratize healthcare.

IoT based Remote Patient Monitoring System

Remote Patient Monitoring (RPM) systems have emerged as transformative tools in healthcare, leveraging technological advancements to enhance patient care beyond traditional clinical settings. This abstract explores the design and implementation of a Remote Patient Monitoring System aimed at bridging the gap between patients and healthcare providers. The system employs a network of wearable devices and sensors to continuously collect and transmit relevant patient data to a secure cloud-based platform. Through real- time monitoring, healthcare professionals can remotely assess vital signs, medication adherence, and other crucial health metrics, enabling early intervention and personalized care. The integration of data analytics and machine learning algorithms further enhances the system's ability to detect patterns, predict potential health issues, and optimize treatment plans. This abstract delves into the key features, benefits, and challenges associated with the Remote Patient Monitoring System, emphasizing its potential to revolutionize healthcare delivery by fostering proactive, patient-centered care and improving overall health outcomes.

Interfering sensed input classification model using assimilated whale optimization and deep Q-learning for remote patient monitoring

The Internet of Medical Things (IoMT)-based Remote Patient Monitoring (RPM) systems provide real-time data and insights about patients' conditions without the need for constant physical visits to healthcare facilities. The wearable sensors are responsible for sensing the psychological vitals of humans at different intervals to detect their precise health status. This article introduces an Interfering Input Classification Model (IICM) for addressing the continuous and discrete data extraction issues in RPM. The proposed model hybridizes whale optimization and deep-Q-learning for classification and connectivity identification between different interval data. The sensed information is segregated as discrete and continuous using the search process termination of the whales. Such classified data is validated for the maximum discreteness and its interconnection between the previous sequences. This interconnection is performed using Q-learning based on different state changes through continuous clinical range correlations. The proposed model exemplifies continuous and discrete signals (data) for abnormality detection and diagnosis recommendation. Thus the proposed model is reliable in improving the classification ratio and accuracy with fewer errors. The experimental values of the proposed model are as follows: IICM enhances classification by 6.65%, signal identification by 6.63%, and classification accuracy by 10.08%. This model reduces classification time by 9.51% and error by 10.14% for different ranges of inputs.

Development and research of a remote patient monitoring system

Development and research of a remote patient monitoring system

Enhancing postoperative anticoagulation therapy with remote patient monitoring: A pilot crossover trial study to evaluate portable coagulometers and chatbots in cardiac surgery follow-up.

Prior research has not assessed the value of remote patient monitoring (RPM) systems for patients undergoing anticoagulation therapy after cardiac surgery. This study aims to assess whether the clinical follow-up through RPM yields comparable outcomes with the standard protocol. A crossover trial assigned participants to SOC-RPM or RPM-SOC, starting with the standard of care (SOC) for the first 6 months after surgery and using RPM for the following 6 months, or vice-versa, respectively. During RPM, patients used the Coaguchek© to accurately measure International Normalized Ratio values and a mobile text-based chatbot to report PROs and adjust the therapeutic dosage. The study assessed patients' and clinicians' experience with RPM and compared direct costs. Twenty-seven patients participated. The median time in therapeutic range (TTR) levels during RPM were 72.2% and 50.6% for the SOC-RPM and RPM-SOC arms, respectively, and during SOC, they were 49.4% and 58.4% for SOC-RPM and RPM-SOC arms, respectively. Patients and the clinical team reported high trust and satisfaction with the proposed digital service. Statistically significant differences were only found in the cost of RPM in the RPM-SOC, which was higher than SOC in the SOC-RPM arm. Portable coagulometers and chatbots can enhance the remote management of patients undergoing anticoagulation therapy, improving patient experience. This presents a promising alternative to the current standard procedure. The results of this study seem to suggest that RPM may have a higher value when initiated after a SOC period rather than starting RPM immediately after surgery.Trial registration: ClinicalTrials.gov NCT06423521.

Telemedicine in the age of the pandemics: The prospects of web-based remote patient monitoring systems for orthopaedic ambulatory care management in the developing economies.

The goal of this research was to demonstrate the efficacy of telemedicine via design, implementation and evaluation of a web-based remote patient monitoring system (WB-RPMS) across the tertiary/university teaching hospitals in a developing country Nigeria, as a tool to continue to expand access to an affordable and resilient tertiary healthcare system through the challenging times of the COVID-19 pandemic or any future disruptions. This research employed an agile and human-centred design thinking philosophy, which saw the researchers iteratively collaborate with clinicians across the system development value chain. It also employed qualitative and quantitative research methods for new system evaluations. After the system's development, a 20-patient sample was randomly selected from members of the National Youth Service Corp to evaluate the WB-RPMS Patient Portal for usability and user experience through a survey based on the system usability scale. Again, the COREQ standards for reporting research result were adopted for this study. The evaluation of the WB-RPMS Patient Portal by a select patient sample showed that 95.0% of the respondents believed that they would like to use the system frequently. It was also discovered that 90.0% of all respondents also indicated that they found the Patient Portal to be simple; 85.0% of the respondents believed and indicated that the WB-RPMS Patient Portal was easy to use. The result of the usability evaluation of the developed WB-RPMS Patient Portal showed that it was well received by the select patient sample and by the clinicians who participated in the development process. In fact, the performance of the system shows that it has the potential to remotely support and sustain improved access to affordable healthcare for outpatients in developing countries even during times of uncertainties and disruptions as recently occasioned by COVID-19 pandemic.

Real-Time Remote Patient Monitoring and Alarming System for Noncommunicable Lifestyle Diseases.

Telemedicine and remote patient monitoring (RPM) systems have been gaining interest and received adaptation in healthcare sectors since the COVID-19 pandemic due to their efficiency and capability to deliver timely healthcare services while containing COVID-19 transmission. These systems were developed using the latest technology in wireless sensors, medical devices, cloud computing, mobile computing, telecommunications, and machine learning technologies. In this article, a real-time remote patient monitoring system is proposed with an accessible, compact, accurate, and low-cost design. The implemented system is designed to an end-to-end communication interface between medical practitioners and patients. The objective of this study is to provide remote healthcare services to patients who need ongoing care or those who have been discharged from the hospital without affecting their daily routines. The developed monitoring system was then evaluated on 1177 records from MIMIC-III clinical dataset (aged between 19 and 99 years). The performance analysis of the proposed system achieved 88.7% accuracy in generating alerts with logistic regression classification algorithm. This result reflects positively on the quality and robustness of the proposed study. Since the processing time of the proposed system is less than 2 minutes, it can be stated that the system has a high computational speed and is convenient to use in real-time monitoring. Furthermore, the proposed system will fulfil to cover the lower doctor-to-patient ratio by monitoring patients from remote locations and aged people who reside in their residences.

A secure framework for IoT‐based healthcare using blockchain and IPFS

AbstractSmart healthcare, also known as IoT (Internet of Things) based healthcare, utilizes IoT technology to enhance the healthcare industry. The use of IoT‐enabled medical equipment enables remote monitoring of patients, allowing for in‐home care and alerting healthcare providers of any changes. This can lead to improved patient outcomes and better disease management. However, it is important to implement robust security measures to protect patient data when using IoT in healthcare. One potential solution is to use blockchain technology to secure data storage and sharing with medical providers. Blockchain's decentralized structure and cryptographic techniques make it difficult for hackers to access or tamper with patient information. Additionally, IPFS (InterPlanetary file system) can be used for efficient data storage and sharing with authorized medical professionals, while smart contract functionality can automate the process of granting and revoking access to patient data. This article proposes a blockchain‐based secure remote patient monitoring system, specifically for chronic disease patients. The system utilizes distributed blockchain for data security, IPFS for data storage and sharing, and DApp for data collection and connection to the blockchain, along with encryption for added security. The proposed approach is compared to existing solutions and found to be a superior option for IoT‐based healthcare.

Design Remote Monitoring System for Patients at Real-Time based on Internet of Things (IoT)

Design Remote Monitoring System for Patients at Real-Time based on Internet of Things (IoT)