Remote Health Monitoring Research Articles

SMHeart ID: smart ID based on ECG and long short term memory for remote health monitoring

SMHeart ID: smart ID based on ECG and long short term memory for remote health monitoring

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.

Fall Detection System based on iOS Smartphone Sensors

Activities of daily living (ADLs) are a crucial aspect of human life, especially in remote health monitoring and fall detection. As smartphones have become an integral part of our daily routines, with their ability to perform complex calculations, connect to the internet, and incorporate various sensors, researchers have been inspired to explore human activity recognition systems. This paper focuses on accelerometer and gyroscope data from iOS-based smartphones. We developed a data collection app to record fall types (e.g., Falling Right, Falling Left) and fall-like activities (e.g., Sitting Fast, Jumping). Volunteers carried smartphones naturally in their pockets during experiments, presenting a challenge of noise but enhancing user comfort. We applied different Machine Learning algorithms (Decision Trees, Random Forest, Logistic Regression, k-Nearest Neighbor, XGBoost, LightGBM, and Neural Networks) to analyze the collected dataset. In contrast to typical studies, our approach replicated real-world smartphone usage. The paper presents and analyzes promising results from the study. Furthermore, we implemented the trained model as a real-time mobile application for potential users. This research illustrates the potential of smartphones in fall detection and opens the way for user-friendly solutions in remote health monitoring.

Measuring Vital Signs such as Temperature, BPM, Humidity using different IOT Sensors and Designing a Mobile Application using Firebase

Abstract: This research describes the creation of an Internet of Things-based smart health surveillance system that allows for the real-time data tracking of a person's vital signs. An ESP8266 Node MCU microcontroller is integrated with temperature, humidity, and heart rate sensors in the system to gather information on the user's body temperature, ambient humidity, and heart rate. A specially created Android mobile application shows the gathered data, which is wirelessly sent to a Firebase cloud database. With the use of the mobile app, users can easily create individualized profiles, view vital sign data, and get warnings. Early health issue diagnosis and proactive illness management are aided by successful implementation, which shows that lowcost IoT devices, cloud computing, and mobile technologies may all be used for effective remote health monitoring

Telehealth Monitoring System for Chronic Disease Management

Telehealth Monitoring System for Chronic Disease Management

Flame system: Computerised cognitive assessment for remote brain health monitoring

Flame system: Computerised cognitive assessment for remote brain health monitoring

SOLDIERS HEALTH MONTORING AND TRACKING USING GSM AND GPS

In enemy territory soldiers not only have to deal with the physical threat, but also with stress andfatigue caused by protracted operations or lack of sleep. So for the security purpose we need a tool for remote soldier performance and health monitoring. So in this project a tool is implemented using biosensors for health monitoring purpose. Also a GPS system is used in order to track the location of soldier. Additionally a GSM modem is also used to make the system wirelessly compatible. Any abnormalities in the readings of wireless body area sensor network (WBASNs) is considered as a trigger for GSM to establish the connection between the soldier and base unit and send current location and health status to the receiver. By using all this equipments we had tried to implement the basic guarding system for the soldier in low cost, light weighted, portable and precise device.

Internet of Medical Things and the Evolution of Healthcare 4.0: Exploring Recent Trends

Enhanced patient care and remote health monitoring have always been important issues. Internet of Medical Things (IoMT) is a subsection of Healthcare 4.0 that uses recent technologies like mobile computing, medical sensors, and cloud computing to track patients' medical information in real-time. These data are stored in a cloud computing framework that may be accessed and analyzed by healthcare experts. IoMT and Healthcare 4.0 have immense potential for revolutionizing patient care and diagnostics, despite facing numerous complex challenges. This paper thoroughly analyzes technical, structural, and regulatory obstacles encountered by the healthcare sector. Challenges in IoMT implementation include cost considerations, network stress, interoperability issues, ethical limitations, policy intricacies, security concerns, and vulnerabilities jeopardizing patient privacy. However, amidst these challenges, the study highlights the prospective long-term benefits, including diminished medical costs and enhanced patient care. In this study, we have portrayed a comprehensive exploration of the field of IoMT and different related technologies from more than 100 papers to represent the transformation and growth in this decade. We have illustrated some of the significant findings of applications and innovations in the domain of IoMT. This paper delves into IoMT's application in dementia detection and care, improved data management, fortified cybersecurity measures, and modernizing existing healthcare systems. The study also offers valuable insights into potential mitigation strategies, offered by ongoing research and innovation to address emerging trends and challenges, propelling the trajectory of Healthcare 4.0 towards an optimized and transformative future for patient well-being. Hence future research needs to integrate more prudent technologies addressing challenges including security, privacy, interoperability, and implementation costs.

Health Monitoring System on Patient Through IOT Nibe Sakshi Babasaheb,

Advancement in technology makes great efforts in health care system. Smart health monitoring will reduce effort of people by automating the patient monitoring task which improves patient work flow management. This study reviews enhancement in health care system. It focuses on portable, wearable and android technology based health monitoring system. This study reveals different approaches for health monitoring. Keywords—mobile health monitoring, wearable health monitoring, remote health monitoring

VLSI design for adjustable compression rate in lossless/lossy compression of EEG signal

In the realm of remote health monitoring, the compression of electroencephalography (EEG) signals at the edge holds significant importance. This paper presents a VLSI structure capable of adjustable compression rates to achieve both lossless and lossy compression of EEG data, catering to environments requiring flexible adjustment of compression types and rates. The architecture integrates two-dimensional discrete wavelet transform (2D-DWT) and the Set Partitioning in Hierarchical Trees (SPIHT). Notably, the Haar wavelet lifting structure is employed in 2D-DWT, and the SPIHT scanning sequence is optimized for enhanced resource efficiency. The proposed algorithm is validated using the CHB-MIT Scalp EEG Database, demonstrating compression ratios of ≥1.95 for normal EEG and ≥1.69 for epileptic EEG. Through synthesis and layout design in the 130 nm CMOS technology, the resulting circuit achieves a maximum clock frequency of 57 MHz and occupies an area of 81 kμm2, with an average power consumption of less than 0.71nj per data for 16-bit data. These findings affirm the efficacy of the proposed VLSI structure for achieving efficient lossless/lossy compression of edge EEG data.

Smart spirits: an integrated IoT-based liquor and health monitoring system

This project entails the development of a comprehensive health monitoring system utilizing Arduino Uno, a range of sensors (including alcohol, pulse, and DHT11), an LCD display, and a GSM module for data transmission to the Thingspeak application. Its objective is to monitor vital health parameters such as temperature, humidity, pulse rate, and alcohol levels in real-time, accessible via the Thingspeak mobile app. Key components include an Arduino Uno microcontroller, DHT11 sensor for temperature and humidity, pulse sensor for heart rate, alcohol sensor for detecting alcohol levels, LCD display for immediate feedback, and a GSM module for remote data transmission. The user-friendly Thingspeak app serves as an interface for seamless health metric monitoring. The system operates by collecting sensor data, displaying it locally on the LCD screen, and simultaneously transmitting it to the Thingspeak app via GSM communication. Users can then access the app to visualize and monitor their health metrics in real-time, allowing for prompt interventions or medical assistance if needed. The code implementation integrates sensor readings, LCD display control, and Thingspeak functionality. Leveraging Thingspeak's cloud-based platform enhances usability and accessibility, making remote health monitoring feasible. This system offers a versatile solution for individuals looking to efficiently track and manage their well-being, thanks to the integration of Arduino Uno, sensors, Thingspeak features, and GSM communication, making it suitable for both local and remote health monitoring applications.

Smart Health Monitoring System

Due to the high inpatient population in hospitals, regular monitoring of inpatients' vital signs is currently a practical concern. As a solution, our proposed system manages the continuous analysis of the vital signs of every inpatient in the general wards, and informs medical professionals in any location at any time about their inpatients' current states in real-time to improve inpatients' health. The suggested system consists of the following arrangements; arrangement for acquiring health readings, identifying the on-duty reported doctors in charge of wards, arrangement for health data exhibiting unit, fall detection, and ECG acquisition. In addition to these arrangements, a website, and an android mobile application were designed to publish measured inpatient vital signs. This proposed product is both novel and different from the existent products because, it comprises of collective arrangements, and is developed in order to assess hospital wards’ inpatients, whereas other systems are designed for remote health monitoring of patients at home. This paper describes the system that was developed and tested successfully. KEYWORDS: Real-time database, Temperature, Heart rate, SpO2, ECG, Fall detection, Website, Mobile application

Remote Sensing-Based Structural Health Monitoring and Damage Assessment

With the rapid development of society and the economy, significant infrastructure, such as roads, buildings, high-speed railways, and bridges, have been built all over the world [...]

A New Smartphone-Based Method for Remote Health Monitoring: Assessment of Respiratory Kinematics

The remote monitoring of clinical parameters plays a fundamental role in different situations, like pandemic health emergencies and post-surgery conditions. In these situations, the patients might be impeded in their movements, and it could be difficult to have specific health monitoring. In recent years, technological advances in smartphones have opened up new possibilities in this landscape. The present work aims to propose a new method for respiratory kinematics monitoring via smartphone sensors. In particular, a specific application was developed to register inertial measurement unit (IMU) sensor data from the smartphone for respiratory kinematics measurement and to guide the user through a specific acquisition session. The session was defined to allow the monitoring of the respiratory movement in five prescribed positions. The application and the sequence were successfully tested on a given population of 77 healthy volunteers. The resulting accelerometers and gyroscope signals were processed to evaluate the significance of differences according to participants’ sex, vector components, and smartphone positioning and, finally, to estimate the respiratory rate. The statistical differences that emerged revealed the significance of information in the different acquisition positions.

Remote mobile health monitoring frameworks and mobile applications: Taxonomy, open challenges, motivation, and recommendations

Advancements in mobile technology have propelled the rapid progress of remote health monitoring, particularly in the domain of mobile health (mHealth). This progress is underscored by sophisticated smart health monitoring applications designed for mobile devices. mHealth monitoring, a standout area within remote health monitoring, excels in providing continuous health monitoring for patients and enabling remote observation by physicians. These applications cater to ambulatory patients with chronic diseases, offering essential features such as Telemedicine services, doctor availabilitys, and emergency ambulance services. Despite the development of various frameworks for mHealth monitoring, existing review papers often fall short in addressing critical parameters and issues related to Telemedicine. Many reviews provide a broad overview but lack the depth needed to dissect intricate aspects like implementation challenges in Telemedicine, nuances in decision-making applications, and the dynamics of mobility for both patients and healthcare providers. Patient rescue protocols, especially in diverse locations, are also underexplored. To fill this gap, our research delves into these dimensions, providing a nuanced understanding of challenges and opportunities in mobile health monitoring, particularly within the Telemedicine context. Leveraging a comprehensive literature review, we meticulously selected over 100 research articles from reputable databases like ScienceDirect, IEEE Xplore, and Springer. We have assessed and ranked the significance of each article. The study aims to address open issues in telemedicine, contributing valuable insights to enhance remote health monitoring.

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.

Telehealth and Pharmaceutical Care for Older People: Today and Tomorrow.

As pointed out in pieces published in this edition of The Senior Care Pharmacist, although health care services have been provided remotely for a long time, it was only with the advent of the internet that health care practitioners and their patients had the means to conduct virtual consultations. Even more sophisticated processes such as remote health monitoring have become available, and indeed to a degree, expected, by patients.

Resource-aware fog service placement using deferred acceptance in edge computing

Fog computing aims at reducing network latency, bandwidth and processing delay by bringing computation resources closer to the data sources. Modern IoT applications, such as smart surveillance systems, remote health monitoring applications, ambient-aware applications etc., are time-critical and computationally intensive. For such applications, it is ideal to consider an intermediate layer of processing i.e a fog layer in addition to cloud. However, processing the workload of such IoT applications with an intermediate layer of fog nodes has several challenges. The significant challenges include the mobility of end-user nodes, dynamic patterns of workload generation leading to highly-variant resource requirements and sensitive quality of service (QOS) requirements. Hence a dynamic approach is required to precisely map the service requests from end-user devices to a suitable fog node. The optimality of a fog node for service placement depends on several factors, including location, resource utilization rates such as CPU, Memory and Disk utilization % and energy consumption. Several meta-heuristic techniques and evolutionary algorithms have been proposed in the past for optimal service placement among fog nodes. This paper investigates the applicability of a game theory-based approach called Deferred acceptance (DA) for the same. The DA algorithm, originally designed as a stable matching mechanism for the marriage market is adapted for service placement problems in fog-based IoT systems. The effectiveness of optimized service placement through the DA approach is validated experimentally. The experiment is carried out by creating a test bed that mimics a fog computing-based intelligent traffic management application. The proposed approach's performance is evaluated through parameters such as the startup time for the DA approach to produce outcomes, total execution time and handshake latency. The numerical results of the DA approach are compared with those of a simple scheduling algorithm ( FIFO) and a basic genetic algorithm-based approach. The DA approach appears to perform on par with the genetic algorithm in terms of start-up time, but exceeds the genetic algorithm with respect to stability of the outcomes produced.

Developing and Implementing an IoT Managed by Electronic Devices for Covid Patient Monitoring via a Secured Communication System

End of 2019 had seen global spread of the deadly coronavirus (SARS-CoV-2) pandemic, which kills people, puts a large portion of the world in danger, and poses a serious threat to all of the world's nations. Leading medical professionals are working extremely hard to identify the virus, develop treatments for it, and create the vaccines that are required to stop and limit its spread. This study intends to develop a low-cost electronic health system to observe patients with covid infections and lessen the work required of clinicians. An enhanced approach for remote health monitoring in hospitals or detention facilities is offered by the internet of things (IoT). The IoT keeps and displays the patient's medical data via a web browser or through specialized apps that offer remote treatment once the sensors collect it. When a patient is in danger, the system offers immediate action to send alarms by email and SMS and to rapidly provide drugs to the patient. Doctors will be updated on each patient's condition thanks to this message.