Remote Health Monitoring System Research Articles

Design of a Model Using Machine Learning and Deep Dyna Q Learning Integration for Improved Disease Prediction in Remote Healthcare

In the domain of proactive healthcare management, the imperative for remote health monitoring has escalated, the remote health care in this scenario specially means, the patient is seating at the remote location that is not in the hospital setting, and doctor or healthcare worker is monitoring the health parameters gathered using biomedical sensors and passed through the network. Conventional methodologies, while partially effective, encounter challenges in predictive precision, responsiveness to evolving health dynamics, and managing the vast array of patient data. These limitations underscore the demand for a sophisticated, holistic solution catering to diverse use cases. This work introduces a pioneering framework amalgamating traditional machine learning (ML) models with the advanced capabilities of Deep Dyna Q Learning process to overcome existing constraints. This framework strategically utilizes ensemble of traditional algorithms which amalgamates the strengths of these diverse models. Central to this model is the integration of Deep Dyna Q Learning, empowering the system with real-time adaptability and dynamic decision-making process through reinforcement learning principles, thereby deriving insights from historical and simulated datasets to foster more nuanced, patient-centric decisions. The impact of this comprehensive approach is profound, evidenced by preliminary results showcasing significant enhancements in the efficiency of remote health monitoring systems. Notably, the model achieves increase in precision, accuracy and recall for disease prediction. These improvements signify a paradigm shift towards proactive and efficient healthcare interventions, especially in remote settings. The fusion of traditional ML techniques with Deep Dyna Q Learning emerges as a potent solution, heralding a revolution in remote health monitoring and establishing a new benchmark for proactive healthcare delivery scenarios.

Healthcare System using IoT and Building Health Monitor App

An improvement in technology and miniaturization of sensors, there have been attempts to utilize the new technology in various areas to improve the quality of human life. One main area of research that has seen an adoption of the technology is the healthcare sector. The people in need of healthcare services find it very expensive this is particularly true in developing countries. As a result, this project is an attempt to solve a healthcare problem currently society is facing. The main objective of the project was to design a remote healthcare system. It’s comprised of three main parts. The first part being, detection of patient’s vitals using sensors, second for sending data to cloud storage and the last part was providing the detected data for remote viewing. Remote viewing of the data enables a doctor or guardian to monitor a patient’s health progress away from hospital premises. The Internet of Things (IoT) concepts have been widely used to interconnect the available medical resources and offer smart, reliable, and effective healthcare service to the patients. Health monitoring for active and assisted living is one of the paradigms that can use the IoT advantages to improve the patient’s lifestyle. In this project, I have presented an IoT architecture customized for healthcare applications. The aim of the project was to come up with a Remote Health Monitoring System that can be made with locally available sensors with a view to making it affordable if it were to be mass produced. Hence the proposed architecture collects the sensor data through Arduino microcontroller and relays it to the cloud where it is processed and analyzed for remote viewing. Feedback actions based on the analyzed data can be sent back to the doctor or guardian through Email and/or SMS alerts

Quality and Safety Standards in Health Services: Development and Implementation

Aim. This research work aims to identify the characteristic features of the development and implementation of quality standards for patient monitoring and safety services in medical institutions, in order to improve quality based on the developed digital model, and describe the requirements for the implementation of quality control according to ISO 9001:2015 and ISO 15189:2022 Standards. Methods. The work uses a digital model of the Traumatology Center, built using the AnyLogic programming environment. This model is designed to identify and analyze effective solutions for implementation, such as patient registration processes, staffing, scheduling of specialized departments, identification of clinical characteristics and early symptoms of diseases to prevent complications during the incubation period. Results. It was discovered that modern wireless technologies and data analytics can be used to develop a remote health monitoring system, enabling early detection of chronic diseases to prevent complications. The scientific novelty of the study consists in the development of a sequence for the implementation of documentation on standardization and quality of service in medical institutions to prevent harm to patients, and improve their safety with the help of a complex of measuring devices and technologies. of the information.

Improving Diabetic Patients Monitoring System Using (NCA-CNN) Algorithm based on loT

The Internet of Things (IoT) and Artificial Intelligence (AI), particularly Machine Learning (ML), have both seen significant advancements in recent years, which has resulted in significant leaps forward in the development of health monitoring systems. Patients may now be prevented, diagnosed, and monitored remotely and at home, eliminating the need to go to health and treatment centers or spend a significant amount of money doing so. This is made possible by advancements in technology. Deep learning has been the primary focus of this research as it relates to the development of a remote health monitoring system for the diagnosis of diabetes. In the system that has been suggested, improvements have been made to both the precision of the detection and the swiftness of the data processing. The Neighbourhood Component Analysis-Convolutional Neural Network (NCA-CNN) approach that we have presented involves two stages: the first stage involves picking the most important features from all of the data, and the second stage involves categorizing the chosen features. The NCA algorithm is a mathematical method that rates the characteristics based on the results of an analysis of the data and picks the most significant aspects. After that, the most salient characteristics are categorized by a deep convolutional neural network, and an accurate diagnosis of the condition is accomplished. According to the findings that were collected, the accuracy of the approach that was suggested is 97.12%.

Smart Solutions for Diet-Related Disease Management: Connected Care, Remote Health Monitoring Systems, and Integrated Insights for Advanced Evaluation

The prevalence of diet-related diseases underscores the imperative for innovative management approaches. The deployment of smart solutions signifies a paradigmatic evolution, capitalising on advanced technologies to enhance precision and efficacy. This paper aims to present and explore smart solutions for the management of diet-related diseases, focusing on leveraging advanced technologies, such as connected care, the Internet of Medical Things (IoMT), and remote health monitoring systems (RHMS), to address the rising prevalence of diet-related diseases. This transformative approach is exemplified in case studies focusing on tailored RHMS capabilities. This paper aims to showcase the potential of three RHMS in introducing a novel evaluation method and their customisation for proactive management of conditions influenced by dietary habits. The RO-SmartAgeing System uniquely addresses age-related aspects, providing an integrated approach that considers the long-term impact of dietary choices on ageing, marking an advanced perspective in healthcare. The NeuroPredict Platform, leveraging complex neuroinformatics, enhances the understanding of connections between brain health, nutrition, and overall well-being, contributing novel insights to healthcare assessments. Focused on liver health monitoring, the HepatoConect system delivers real-time data for personalized dietary recommendations, offering a distinctive approach to disease management. By integrating cutting-edge technologies, these smart solutions transcend traditional healthcare boundaries.

Innovative non-contact r-r intervals estimation using viterbi algorithm with Squared Branch Metric (VSBM)

Non-contact heartbeat detection by using Doppler sensor is a critical component of remote health monitoring systems, enabling continuous and unobtrusive monitoring of an individual’s cardiovascular health. In this paper, we reported an innovative approach for non-contact heartbeat detection using the Viterbi algorithm, leveraging the distribution of the difference of two adjacent R-R intervals (RRIs). RRIs represent the time between successive peaks in the electrocardiogram (ECG) signal and are fundamental in analyzing heart rate variability, mental stress conditions and heart diseases. Numerous non-contact Doppler sensor-based methods have been proposed for heartbeat detection, leveraging the evaluation of RRIs without physical device attachment. However, challenges arise from unwanted peaks introduced by respiration and slight body movements, even when the subject remains motionless with normal breathing. This study presented an innovative approach for selecting heartbeat peaks utilizing the Viterbi algorithm with the squared difference of two adjacent RRIs as the Branch metric (BM). Our preliminary experiments reveal that the difference between two adjacent RRIs closely follows a Gaussian distribution. Building upon this observation, we considered the Viterbi algorithm with Squared Branch Metric (VSBM) to estimate the heartbeat accurately. To assess the accuracy of our peak selection method, we conducted experiments comparing it with two existing peak detection methods: (i) Doppler output after Low-Pass Filter (LPF)-based method and (ii) Spectrogram-based method. Our results demonstrate that the proposed VSBM method is effective to detect the heartbeat accurately for each peak detection method. Furthermore, we compared the performance of “Spectrogram + VSBM” outperforms the “Doppler output after LPF + VSBM” method by the Root-Mean-Square Error (RMSE) of RRIs.

Cyber Security Challenges and Effective Security Measures for IOT-based Intelligent Healthcare Systems

Aims and Background: In order to keep up with the rapid development of technology, cities must enhance their offerings in the areas of public security, medical care, transportation, and citizen well-being. Healthcare systems rely heavily on patient care. Patients' life and the services offered by doctors, nurses, clinicians, pharmaceutical companies, and governments stand to benefit from the implementation of Healthcare IoT. There has been a change in the medical industry, marked by the widespread adoption of wireless healthcare-monitoring-systems (HMS) in hospitals. Objectives and Methodology: However, the Internet-of-Things(IoT) concept frequently ignores security-privacy for linked things. Security and privacy protections must be implemented systematically throughout the entire healthcare and remote health monitoring system, from the creation of equipment to their interconnection, communication, storage, and eventual destruction. This research investigates the risks associated with using smart health devices and provides recommendations for addressing those risks. The present security and privacy of IoT devices in healthcare systems, as well as the difficulties in adopting security frameworks, are discussed, and recommendations for addressing these issues are offered. Results and Conclusions: This study also contributes in two ways to the ongoing effort to investigate security-privacy concerns in the circumstance of smart cities for healthcare purposes. Moreover, this article provides a summary of the many Internet of Things applications and the cyber threats they face. On the other hand, thorough assessments of methods to reduce cyber threats to 60% are presented.

Examining medical urgent and patient precedence within the telemedicine landscape with its infrastructure and policies: A comprehensive review

Medical facilities are confronted with grave issues, including a population that is aging and a physician shortage. In an effort to address these issues, telehealth and remote health monitoring systems (RHMS) aim to reduce hospital visits by a small amount. RHMS lessens the workload for primary care patients and enhances inter-unit communication, which lessens the strain on emergency rooms. Due to significant advancements in mobile information transfer and processing of signal technologies, some healthcare researchers have made efforts to employ RHMS in place of hospital visits to provide triage and prioritization for individuals. Prioritization is a strategy for giving people urgent medical care in an attempt to save their lives, while clinical examination determines the severity of a sickness or damage. To emphasize the disadvantages of the present patient screening and prioritization system over the telehealth environment, a crucial inquiry is needed. Based on two crucial axes, an in-depth examination of patient prioritization and medical crisis assessment in a videoconferencing setting was provided in this research. First, a collection, analysis, and classification of earlier research on triage of patients and prioritization in this kind of setting was done. Second, a variety of priorities and standards, as well as various approaches and procedures for prioritization, were provided and examined. The subsequent outcomes were attained: The shortcomings and issues with the current patient prioritization and assessment methods were highlighted. The assessment and priority for individuals who have cardiac conditions were not presented. In the years to come, a structure based on the theory of evidence, as well as the incorporation of a multiple-layer analytical hierarchy approach and method for ranking preference due to resemblance to perfect solutions, can be used to prioritize the care of several patients with ongoing cardiovascular disease by triaging them based on various emergencies dimensions.

Development of an IoT-Based Automatic Remote Health Monitoring System

A well-planned health monitoring system is essential for the modern health center, and a portable remote health monitoring system with many intellectual features is becoming a great improvement in healthcare arrangements. The proposed system includes various micro-electronics medical devices and applications that come through network-connected devices and help to monitor patients' real-time medical data. In this paper, the patient's health condition has been monitored by using the four types of major health parameters temperature sensor, heart pulse rate sensor, blood pressure sensor module, and blood oxygen (SpO2) sensor. All the sensor nodes are connected to the Raspberry Pi-based embedded system. The real-time health data are recorded, stored, and transmitted to the doctor via the internet with the help of a Raspberry Pi-based embedded system and IoT server. The system will benefit the patient from remote areas with quick diagnosis, remote observation, home observation, and a medical data storage system. Journal of Engineering Science 14(2), 2023, 21-30

Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error

Inter-beat interval (IBI) measurement enables estimation of heart-tare variability (HRV) which, in turn, can provide early indication of potential cardiovascular diseases (CVDs). However, extracting IBIs from noisy signals is challenging since the morphology of the signal gets distorted in the presence of noise. Electrocardiogram (ECG) of a person in heavy motion is highly corrupted with noise, known as motion-artifact, and IBI extracted from it is inaccurate. As a part of remote health monitoring and wearable system development, denoising ECG signals and estimating IBIs correctly from them have become an emerging topic among signal-processing researchers. Apart from conventional methods, deep-learning techniques have been successfully used in signal denoising recently, and diagnosis process has become easier, leading to accuracy levels that were previously unachievable. We propose a deep-learning approach leveraging tiramisu autoencoder model to suppress motion-artifact noise and make the R-peaks of the ECG signal prominent even in the presence of high-intensity motion. After denoising, IBIs are estimated more accurately expediting diagnosis tasks. Results illustrate that our method enables IBI estimation from noisy ECG signals with SNR up to -30 dB with average root mean square error (RMSE) of 13 milliseconds for estimated IBIs. At this noise level, our error percentage remains below 8% and outperforms other state-of-the-art techniques.

Cushy and Cheap Smart Health Monitoring System Incorporating a Ventilator

The handiness of mercantile mechanical ventilators is highly limited in low-income countries due to high rate in the price of its affordability. This factor affects the treatment of so many patients suffering from chronic respiratory syndrome. In this research, a low- cost and easy to use pressure ventilator coupled with a remote health monitoring system to assist the doctors check up on the patients is developed. There is need to maximize the availability of the ventilators in low-income countries at an affordable price tag. This system was developed with the aid of an Arduino nano, ESP8266 based NodeMcu microcontroller, high pressure blower, pressure transducers, humidity and temperature sensor. The developed system was evaluated and compared with existing one, using an actively breathing patient simulator as it mimics through a range of respiratory diseases. In the end, we were able to design a low-cost smart health monitoring system that incorporates ventilator and health vitals monitor. Our Design was implemented and verified to be working and equally satisfying all the specified requirements.

Blockchain-Based Logging to Defeat Malicious Insiders: The Case of Remote Health Monitoring Systems

Blockchain-Based Logging to Defeat Malicious Insiders: The Case of Remote Health Monitoring Systems

Wireless Body Area Network (WBAN) based Health Care Monitoring: A Comprehensive Review

The Internet of Things (IoT) based Wireless Body Area Networks (WBAN), play a very important role in the implementation of IoT devices. It is an upcoming research area in which we implement new advancement techniques, such as various algorithms. In this research, we explore how to secure IoT devices from cyber-attacks and also define how to handle the communication packets between IoT devices. Some IoT devices require more resources for communication so we define the techniques used to reduce these resources. The main purpose of this review is to provide a quick review of all techniques for novice researcher who wants to start their research in this domain. Now the universe is getting smaller due to a sharp growth in scientific and technological research. The development of communication technologies over the past few decades is remarkable. It has gotten harder for medical associates to take care of the patients by coming into the hospital and then personally checking them. The population of the world is increasing and the area is getting smaller. The development of wireless communication has provided a solution to these issues in the form of a remote health monitoring system. The WBAN in the domain of medicine provides the best way for patient monitoring inside or outside the hospital. Through WBAN, we can easily monitor the patient's state without visiting the hospital. WBAN is used in multiple areas such as energy-saving, eHealth monitoring systems to maintain the quality services of data, and many other areas. In this paper, we survey the current state of various aspects of WBAN technologies that are being used in healthcare applications, the targeted area is the eHealth monitoring system. Using multiple WBAN techniques, we aim to identify which techniques are giving better results in data transmission, storing, and maintaining patient data privacy and also identify the best performance using multiple techniques based on the two most important parameters such as ‘throughput’, and ‘delay’. These parameters estimate the overall performance of any WBAN system. We collected data from the last 15 years' papers. The major contribution of this paper is to identify the best technique in e-healthcare among all in terms of delay and throughput used in the past recent years.

Movement Digital Biomarkers for Functional and Cognitive Decline in Dementia

AbstractBackgroundDigital biomarkers based on accurate tracking of motor behaviour can provide a cost‐effective, objective, and robust measure for disease progression in dementia, changes in care needs, and the effect of interventions. Most of our brain is involved in the planning and execution of movement. Hence, quantifying and monitoring movement can produce powerful indicators of both motor and cognitive decline. In this project, we develop an intelligent system that computes and tracks movement‐based digital biomarkers of dementia.MethodsThe project is established in a controlled and ecologically valid mock apartment – the Living Lab – situated in the UK DRI Care Research and Technology Centre. We collected patients’ performance in a set of physical tasks and activities of daily living (ADL) with a depth‐based motion capture system, along with neurocognitive measures using Addenbrooke’s Cognitive Examination III and Cognitron. We first conducted temporal and spatial synchronisations of data collected from six Microsoft Azure Kinect cameras to produce high‐precision three‐dimensional joint coordinates that provide dynamic postural representations. These were then used to compute digital kinematic markers. This was followed by an assessment of the efficacy of the biomarkers in characterising people affected by dementia. We examined the association between the biomarkers with motor and cognitive function and overall efficiency in performing ADL.ResultsOur preliminary analysis showed that patients and healthy controls can be distinguished by the distribution of extracted features (an example feature – spinal curvature – is shown in Figure 1) in structured tasks and free behaviour. The variance of the distribution was correlated with performance in clinical assessments.ConclusionOur analysis suggests the feasibility of digital biomarkers in characterising functional limitations and cognitive decline. Successfully validated biomarkers can be integrated into remote health monitoring systems at home, care‐homes, and clinics, with the potential to revolutionise prognostic prediction and functional assessment of dementia.

Проблемные вопросы разработки системы удалённого мониторинга здоровья

Summary. The aim of the study is to develop a remote patient health monitoring system. The research methodology includes three main parts: the first part is detecting the fact that the patient has fallen; the second part is taking electrocardiogram — ECG (heartbeat detection); the third part is transmitting the data for its remote viewing by a medical professional. Results of the study and their analysis. The main objective of the study was achieved. All three individual modules — patient fall detection module, heartbeat detection module and remote viewing module — gave the planned results. It is concluded that all the modules of the system can be further optimized and brought to a final unified scheme.

Advancing structural health monitoring: A vibration-based IoT approach for remote real-time systems

In the modern world, civil engineering structures are of paramount importance. Early damage detection through continuous health monitoring, instead of conventional periodic inspection, is the promising way forward for healthy structures with the least maintenance requirement. The damage is identified at the initial stage, so the cost associated with its repair can be reduced. Considering the immense scale of civil infrastructure, currently, available structural health monitoring systems are not feasible for mass application as they are prohibitively expensive. Hence, it is the need of the hour to develop a remote real-time Structural Health Monitoring System (SHMS). In the current study, a system is proposed that utilizes a vibration-based structural health monitoring approach and extracts dynamic parameters from acceleration data. The system consists of a node tasked with data collection and transmission and a server assigned with data post-processing and results display. The node comprises the following main hardware components: ESP32 as a microcontroller unit and ADXL345 as an accelerometer. The system was developed with the capability of multiple node integration, onboard data storage to eliminate sampling rate inconsistencies, dual trigger modes to achieve a high signal-to-noise ratio (SNR), power usage optimization to prolong stand-by time, and wireless data transmission along with automated post-processing. To minimize the cost of the system, Internet of Things (IoT) technology was adopted to develop the system.

A Review of Recent Innovations in Remote Health Monitoring.

The development of remote health monitoring systems has focused on enhancing healthcare services' efficiency and quality, particularly in chronic disease management and elderly care. These systems employ a range of sensors and wearable devices to track patients' health status and offer real-time feedback to healthcare providers. This facilitates prompt interventions and reduces hospitalization rates. The aim of this study is to explore the latest developments in the realm of remote health monitoring systems. In this paper, we explore a wide range of domains, spanning antenna designs, small implantable antennas, on-body wearable solutions, and adaptable detection and imaging systems. Our research also delves into the methodological approaches used in monitoring systems, including the analysis of channel characteristics, advancements in wireless capsule endoscopy, and insightful investigations into sensing and imaging techniques. These advancements hold the potential to improve the accuracy and efficiency of monitoring, ultimately contributing to enhanced health outcomes for patients.

Design and Implementation of an Open-Source and Internet-of-Things-Based Health Monitoring System

Across the globe, COVID-19 had far-reaching impacts that included healthcare facilities, public health, as well as all forms of transport. Hospitals were experiencing staffing shortages at the same time as patients were experiencing healthcare issues. Consequently, even in developing countries without full access to technology, remote health monitoring became necessary. There was a greater severity of the pandemic in countries with fewer financial and technical resources. It became evident that such remote health monitoring systems that not only allowed the user to monitor their basic health information, but also to communicate that information to healthcare personnel, were essential. In this article, we present an open-source, Internet-of-Things (IoT)-based health monitoring system that is intended to mitigate the basic healthcare challenges posed by remote areas of developing countries. To facilitate remote health monitoring, an IoT server has been configured on an ESP32 chip as part of this study. The microcontroller was also connected to a Max 30100 sensor, a DHT11 sensor, and a global positioning system GPS module. As a result of this, the user is able to measure the heart rate (HR), blood oxygen level (SpO2), human body temperature, ambient temperature and humidity, as well as the location of the user. Through the internet protocol, the important vital signs can be displayed in real time on the dashboard using a private communication network. This article presents the details of a complete system design, implementation, testing, and results. Such systems can help limit the spread of infectious diseases like COVID-19.

‘That’s when I put it on’: stakeholder perspectives in large-scale remote health monitoring for older adults

Remote health monitoring (RHM) provides various benefits to older adults, but its use is still limited. Remote monitoring may help in avoiding emergencies and prolong users’ independence. To understand how to design systems which support older adults, we studied a large-scale remote health monitoring system. The system used fitness-grade smartwatches to monitor the vital signs of more than 2000 users constantly. To probe the lived experience of using RHM, we conducted an explorative interview study (N = 41) with operators, carers, and users of the RHM system. Our thematic analysis reveals that personalisation of care ecology is crucial for developing users’ confidence and trust in the system. We found that participation in RHM may catalyse positive changes in older adults’ lifestyles. Based on our findings, we formulate five recommendations for designing future health monitoring systems. Our work contributes to insights into the lived experience and stakeholder ecology of health monitoring systems.

IoT-based health care system for emotional and heart rate monitoring

An IoT-based remote health monitoring system helps patient’s especially older adults and non-communicable disease (NCD) patients to get medical assistance or advice without being physically present in the hospital or medical centre. This system provides regular health monitoring from anywhere by having access to the Internet. One of the important elements is the physiological parameters which indicate the health status of the monitored patients. Literature study shows that most of the previous works monitor blood pressure, respiratory rate, temperature, ECG, blood oxygenation, diabetes, and hypertension. There have been little works paid attention to measuring the emotional level of the patient. Therefore, this paper explains the development of an IoT-based health monitoring system that measures the emotional level of the patient apart from other essential physiological parameters. The system consists of a microcontroller, two sensors, a wireless communication module, and an IoT cloud display, focusing on measuring the heart rate, body temperature, SpO2, and emotional level. For the heart rate and body temperature readings, the measurements acquired from the developed system show a close agreement with those of the established brand ROSSMAX with an error of less than 5%. For the emotional level test, there are 83.3% out of twelve subjects agree that the GSR value shows her feeling.