Remote Health Monitoring System Research Articles

On the design of Remote Health Monitoring System

With 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 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. With improvement in technology previously expensive hospital equipment have been redesigned using current technology. The developments have seen a trend known as remote healthcare or previously known as Telemedicine. As a result, this paper is an attempt to solve a healthcare problem facing the society. The main objective of the paper is to design a remote healthcare system. It is comprised of three main parts. The first part being detection of a fall, second being detection of electrocardiogram commonly referred to as ECG or EKG( heartbeat detection) and the last part is providing the detected data for remote viewing. Remote viewing of the data enables a doctor or health specialist to monitor a patient’s health progress away from hospital premises.

IoT-based GPS assisted surveillance system with inter-WBAN geographic routing for pandemic situations

BackgroundWorldwide pandemic situations drive countries into high healthcare costs and dangerous conditions. Hospital occupancy rates and medical expenses increase dramatically. Real-time remote health monitoring and surveillance systems with IoT assisted eHealth equipment play important roles in such pandemic situations. To prevent the spread of a pandemic is as crucial as treating the infected patients. The COVID-19 pandemic is the ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). MethodsWe propose a surveillance system especially for coronavirus pandemic with IoT applications and an inter-WBAN geographic routing algorithm. In this study, coronavirus symptoms such as respiration rate, body temperature, blood pressure, oxygen saturation, heart rate can be monitored and the social distance with ‘mask-wearing status’ of persons can be displayed with proposed IoT software (Node-RED, InfluxDB, and Grafana). ResultsThe geographic routing algorithm is compared with AODV in outdoor areas according to delivery ratio, delay for priority node, packet loss ratio and bit error rate. The results obtained showed that the geographic routing algorithm is more successful for the proposed architecture. ConclusionThe results show that the use of WBAN technology, geographic routing algorithm, and IoT applications helps to achieve a realistic and meaningful surveillance system with better statistical data.

Remote Patient Health Monitoring System Using Mobile and Wireless Body Area Network in Yemen

The traditional approach for monitoring chronic diseases represents a major challenge for hospitals and the Ministry of Health in Yemen. This research aims to develop a Remote Patient Health Monitoring System (RPHMS) using Bluetooth enabled medical sensors and mobile phones. This research adopts the Rational Unified Process (RUP) is a software development process framework to develop the proposed system. The RPHMS provides real-time monitoring for the patients suffering from chronic diseases and manages the patients' medical record in which the diagnostic and prescriptive data are stored for further analysis. The RPHMS consists of two major subsystems: Mobile Patient System (MPS) to wirelessly measure the patient's medical and position data, and automatically send the data to the central hospital server; and Central Hospital System (CHS) to automatically store the diagnostic data in the hospital database and then forward the data in form of Short Message Service (SMS) to the doctor. In addition, the CHS enables the patient to get feedback from the doctor. Moreover, CHS stores the feedback and the prescriptions in the hospital database. These data can be accessed by the patient using a mobile application. The results show that RPHMS enables hospitals and doctors to obtain accurate and timely data regarding patients' chronic illnesses. This research concludes that the RPHMS improves the control of the heart rate, blood pressure, and oxygen percentage in the blood (SPO2), since it provides real-time monitoring of chronic patients.

Patch Array Antenna for Health Care and Monitoring System

Health Care is the biggest challenge in today's world. There is a huge gap between the population and the number of health care professionals. One of the possible solutions can be a remote health monitoring system which is better known as telehealth. This paper deals with the design of antenna which will serve as the transmitter unit of the health care monitoring system which is fitted with the patient’s body. The paper describes a Swastik shaped antenna operating at a frequency of 3.5 GHz. The designed antenna shows efficiency of 93.8% with a simulated gain of 9.08 dB and a reflection coefficient of -23.50 dB. In this work, the two sides of Swastik (right and left part) are designed separately and then both were merged to have a complete design. The results show a considerable increase in the gain of the overall antenna as compared to the right side or left side of the swastika design.

Demonstration Experiment of using AI Remote Health Monitoring System “Anshin-net” for Early Detection and Prevention of Aggravation for Disease whose Aged People Live in the Nersing Home

Demonstration Experiment of using AI Remote Health Monitoring System “Anshin-net” for Early Detection and Prevention of Aggravation for Disease whose Aged People Live in the Nersing Home

Undergraduate Students’ Stress, Anxiety and Depression Remote Health Monitoring System

Mental health such as stress, anxiety and depression have been on the rise in recent time among different groups of people in the emerging challenging society and especially among university students. University is a place where most adolescents would experience a change phase in life, discovering their identity as well as experiencing several challenges. All these pressures might cause students to suffer from anxiety or stress or in some cases severe depression, which could lead them to isolate themselves from other people, or to have poor academic performance or very low self-esteem as well as dropping out from the universities. This is quite a dangerous trend because if such students are not treated early, the chances for their situation to worsen or ultimately attempting suicide would be high. Several technologies have been utilised to help assist clinicians and patients to confront and help mitigate health problems. Technologies such as eHealth, or mHealth which utilises smartphones and remote patient monitoring systems (RPMS) have been widely used in the healthcare and are proven to be efficient and quite reliable to help those who suffer from health issues. In this project, a remote mental health monitoring system(RMHMS) has been proposed to enable lecturers as well as university administration and therapists/counsellors to track students’ level of stress, anxiety and depression and swiftly engage those who suffer from stress, anxiety and depressive symptoms. Students who suffer from severe stress, anxiety symptoms and depression, will be given more attention by the university’s administrations based on the information available from the remote mental health monitoring system. The proposed system will help facilitate early interventions and treatment such as referrer to counselling sessions or seeking psychiatric help. The system equally incorporates alert systems to students’ friends or families and their caregivers to intimate them of their situation too so that they may help the students to overcome their mental health issues before getting worsened.

Real-Time Remote Health Monitoring System Driven by 5G MEC-IoT

Telemedicine over Internet of Things (IoT) generates an unprecedented amount of data, which further requires transmission, analysis, and storage. Deploying cloud computing to handle data of this magnitude will introduce unacceptable data analysis latency and high storage costs. Thus, mobile edge computing (MEC) deployed between the cloud and users, which is close to the nodes of data generation, can tackle these problems in 5G scenarios with the help of artificial intelligence. This paper proposes a telemedicine system based on MEC and artificial intelligence for remote health monitoring and automatic disease diagnosis. The integration of different technologies such as computers, medicine, and telecommunications will significantly improve the efficiency of patient treatment and reduce the cost of health care.

Identification of Human Breathing-States Using Cardiac-Vibrational Signal for m-Health Applications

In this work, a seismocardiogram (SCG) based breathing-state measuring method is proposed for m-health applications. The aim of the proposed framework is to assess the human respiratory system by identifying degree-of-breathings, such as breathlessness, normal breathing, and long and labored breathing. For this, it is needed to measure cardiac-induced chest-wall vibrations, reflected in the SCG signal. Orthogonal subspace projection is employed to extract the SCG cycles with the help of a concurrent ECG signal. Subsequently, fifteen statistically significant morphological-features are extracted from each of the SCG cycles. These features can efficiently characterize physiological changes due to varying respiratory rates. Stacked autoencoder (SAE) based architecture is employed for the identification of different respiratory-effort levels. The performance of the proposed method is evaluated and compared with other standard classifiers for 1147 analyzed SCG-beats. The proposed method gives an overall average accuracy of 91.45% in recognizing three different breathing states. The quantitative analysis of the performance results clearly shows the effectiveness of the proposed framework. It may be employed in various healthcare applications, such as pre-screening medical sensors and IoT based remote health-monitoring systems.

Biometric Gloves for Health Monitoring

Global health, defined by the access to healthcare in every region, is symbolized by its demands to be economical and easily accessible. IOT based remote health monitoring system, advancing in the health care sector, is one of the most up-and-coming technological interventions. A serious concern world over, has been perilous work environment that pose health and safety hazards. With the advancements in technology, it is now possible to supervise individual health parameters and provide comprehensive information on health conditions. Users vital information can be monitored from anywhere with access to a specific control centre where the information is stored in real time. In this paper we present a remote health monitoring system that uses an IOT based smart edge technology, where wearable vital sensors transmit data to the cloud using a Wi-Fi module.

Intelligent health monitoring system modeling based on machine learning and agent technology

The use of machine learning approach in the field of health informatics improves the quality and effectiveness of decision-making process. Its particular integration into the Remote Health Monitoring Systems (RHMS) allows: collecting, analyzing and learning from real-time data, automatically gainin g knowledge and making predictions on the patients state. The RHMS represent an effective solution to control and monitor a growing number of dependent or elderly patients. They showed impressive results in healthcare applications. These systems are built around the sensors installed on the body of the patient and others embedded in their environments. In this context, we are confronted with the challenge of temporal aspect of data. To meet this challenge, we propose that RHMS combine machine learning for generating intelligent valuable information, and visual analytics for gaining insight the collected real-time data. The success of these applications is based on the quality of their design and development. For this reason, we propose to design a RHMS using multi-agent technology. The developed prototype was evaluated to verify our proposal feasibility.

REMOTE HEALTH MONITORING OF LONG-TERM SEIZURE UNDER STRESS

Healthcare field is developing rapidly with advancement of technology and improved quality of service. Remote health monitoring is one of the major areas developing at a faster rate due to its advantages over the traditional methods of treatment in hospital. With the advancement of sensor and communication technologies patients at home can be monitored easily. In this paper, a remote health monitoring system is proposed to detect long-term seizure at home environment. Sensors such as Electroencephalograph (EEG), Electrocardiogram (ECG), accelerometer, heart rate sensor, respiration rate sensor, skin temperature and blood pressure sensor are used to measure the long-term seizure at home. In the proposed system these sensors are placed in a wearable device so that the patients can do their daily routines without any discomfort. The important issues present in the long-term seizure detection under stress are discussed and a wireless wearable remote health monitoring system to overcome these issues is proposed.

Achieving sustainability through smart city applications: protocols, systems and solutions using IoT and wireless sensor network

Smart city applications is not just limited to the use of technology in building better cities and improving the citizens’ quality of lives. In reality, it is capable of building comprehensive solutions for the development of sustainable communities, irrespective of being urban or rural. Different communities will have their own dynamics and complexity, some communities are widespread and distributed, while some are more dense and concentrated. Each of these characteristics can either become an advantage or a barrier while developing solutions for these communities, which can be addressed with the proper use of technology. Internet of things (IoT) and wireless sensor networks (WSN), are the technologies in particular that can help in building comprehensive solutions for creating sustainable communities as they have a wide variety of applications such as disaster management, healthcare, energy, water, surveillance, structural health monitoring and environmental monitoring. This paper describes about four major research works in smart city application areas such as (a) design and deployment of an IoT system for early warning of landslides, (b) heterogeneous wireless communication design and optimization for enabling real-time management of interoperation of heterogeneous devices and data services in a hybrid electrical network, (c) development of (low-cost) IoT sensors for remote patient monitoring and (d) design and deployment of systems and solution for providing mobile offshore connectivity for fishing vessels—internet over sea. This paper details the current state of the art, challenges to be solved, and the methodology that we have adapted for developing solutions in all these research areas. The extensive research in IoT, WSN and its applications has resulted in receiving the award, titled “World Center of Excellence in Landslide Risk Reduction” and five patents, namely (a) network-based system for predicting landslides and providing early warnings (b) mobile infrastructure for coastal region offshore communications and networks (c) wearable wireless tongue controlled devices (d) Systems, methods, and devices for remote health monitoring and management (e) Systems, methods, and devices for remote health monitoring and management using internet of things sensors

Proposed Modifications in the 6-Minute Walk Test for Potential Application in Patients With Mild COVID-19: A Step to Optimize Triage Guidelines.

Proposed Modifications in the 6-Minute Walk Test for Potential Application in Patients With Mild COVID-19: A Step to Optimize Triage Guidelines.

Enhancing Energy in WBAN through Cognitive Radio Networks

Wireless Body Area Networks (WBAN) provides to measure various physiological and biological data and monitor human body functionalities. In this study, a cognitive radio based coordinator node is designed for Wireless Body Area Networks. Cognitive Radio is capable of connecting various wireless access points with perception and adaptation features. A simulation model has been developed for implementing cognitive radio enabled body area network for analyzing remote health monitoring system for humans. The prominent parameters such as user speed, access point delay, and connection cost are taken into account when selecting the wireless access point. A supervised machine learning technique called Particle Swarm Optimization (PSO) is adapted in the proposed model for improving Energy efficiency and reducing transmission delay in CR enabled WBAN. Simulation is performed using MATLAB and results indicate that the use of proposed PSO for WBAN performs efficiently compared to traditional methods based on MAC protocols.

A Decentralized Peer-to-Peer Remote Health Monitoring System.

Within the Internet of Things (IoT) and blockchain research, there is a growing interest in decentralizing health monitoring systems, to provide improved privacy to patients, without relying on trusted third parties for handling patients’ sensitive health data. With public blockchain deployments being severely limited in their scalability, and inherently having latency in transaction processing, there is room for researching and developing new techniques to leverage the security features of blockchains within healthcare applications. This paper presents a solution for patients to share their biomedical data with their doctors without their data being handled by trusted third party entities. The solution is built on the Ethereum blockchain as a medium for negotiating and record-keeping, along with Tor for delivering data from patients to doctors. To highlight the applicability of the solution in various health monitoring scenarios, we have considered three use-cases, namely cardiac monitoring, sleep apnoea testing, and EEG following epileptic seizures. Following the discussion about the use cases, the paper outlines a security analysis performed on the proposed solution, based on multiple attack scenarios. Finally, the paper presents and discusses a performance evaluation in terms of data delivery time in comparison to existing centralized and decentralized solutions.

Multi-Sensor Time-Series Classification for Activity Tracking Under Variable Length

Recent advances in mobile devices, wearable sensors and data analytics are enabling greater adoption of remote health monitoring systems, where human activity recognition (HAR) plays a key role. Most HAR systems assume that consecutive data segments are independent and aim at accurately classifying each individual data segment. However, because training and operating contexts are often different, and collecting labels can be time-consuming and expensive, the recognition accuracy is typically considerably lower in the new (operating) context. In this paper, we propose a framework for HAR that has several unique capabilities: 1) Turns predictions into useful information (e.g. daily distribution of activities for a user) that could be assessed by a clinician or health professional. Our goal is to mitigate the effects of variation between a training and an operating context. 2) Leverages the dependence between consecutive data samples to perform robust activity recognition. And 3) Performs activity classification for variable length set of activities. The framework we present encompasses two main modules. The first is a Recurrent Long Short-Term Memory deep neural network, the core component which gives us the capability to capture the dependence between consecutive sensor data samples and generate labels for every micro segment. The second is a post-processing step performed for uncertainty filtering and label smoothing of decisions. The filtering method enables the system to recognize activities in the presence of difference in the training and testing data distributions, and the smoothing method makes the system properly capture the distribution of activities. We demonstrate that our deep learning architecture can achieve 90% accuracy in standard training scenarios. Furthermore, we show that, in the case of inter-subject variation, the two methods allow us to still accurately capture the distribution of activities.

Attack Graph Modeling for Implantable Pacemaker.

Remote health monitoring systems are used to audit implantable medical devices or patients’ health in a non-clinical setting. These systems are prone to cyberattacks exploiting their critical vulnerabilities. Thus, threatening patients’ health and confidentiality. In this paper, a pacemaker automatic remote monitoring system (PARMS) is modeled using architecture analysis and design language (AADL), formally characterized, and checked using the JKind model checker tool. The generated attack graph is visualized using the Graphviz tool, and classifies security breaches through the violation of the security features of significance. The developed attack graph showed the essentiality of setting up appropriate security measures in PARMS.

Motion-to-BMI: Using Motion Sensors to Predict the Body Mass Index of Smartphone Users.

Obesity has become a widespread health problem worldwide. The body mass index (BMI) is a simple and reliable index based on weight and height that is commonly used to identify and classify adults as underweight, normal, overweight (pre-obesity), or obese. In this paper, we propose a hybrid deep neural network for predicting the BMI of smartphone users, based only on the characteristics of body movement captured by the smartphone’s built-in motion sensors without any other sensitive data. The proposed deep learning model consists of four major modules: a transformation module for data preprocessing, a convolution module for extracting spatial features, a long short-term memory (LSTM) module for exploring temporal dependency, and a fully connected module for regression. We define motion entropy (MEn), which is a measure of the regularity and complexity of the motion sensor, and propose a novel MEn-based filtering strategy to select parts of sensor data that met certain thresholds for training the model. We evaluate this model using two public datasets in comparison with baseline conventional feature-based methods using leave-one-subject-out (LOSO) cross-validation. Experimental results show that the proposed model with the MEn-based filtering strategy outperforms the baseline approaches significantly. The results also show that jogging may be a more suitable activity of daily living (ADL) for BMI prediction than walking and walking upstairs. We believe that the conclusions of this study will help to develop a long-term remote health monitoring system.

Evaluation of Remote Monitoring Device for Monitoring Vital Parameters against Reference Standard: A Diagnostic Validation Study for COVID-19 Preparedness.

Context:Vital parameters including blood oxygen level, respiratory rate, pulse rate, and body temperature are crucial for triaging patients to appropriate medical care. Advances in remote health monitoring system and wearable health devices have created a new horizon for delivery of efficient health care from a distance.Materials and Methods:This diagnostic validation study included patients attending the outpatient department of the institute. The accuracy of device under study was compared against the gold standard patient monitoring systems used in intensive care units.Statistical Analysis:The statistical analysis involved computation of intraclass correlation coefficient. Bland–Altman graphs with limits of agreement were plotted to assess agreement between methods. P <0.05 was considered statistically significant.Results:A total of 200 patients, including 152 males and 48 females in the age range of 2–80 years, formed the study group. A strong correlation (intraclass correlation coefficient; r > 0.9) was noted between the two devices for all the investigated parameters with significant P value (<0.01). Bland–Altman plot drawn for each vital parameter revealed observations in agreement from both the devices.Conclusion:The wearable device can be reliably used for remote health monitoring. Its regulated use can help mitigate the scarcity of hospital beds and reduce exposure to health-care workers and demand of personal protection equipment.

IoT Based Remote Health Monitoring System

As the technology changing every year so, there have been an attempts to utilize the new technology in various areas to improve the quality of human life. One of the major field of research that has seen an adoption of the technology is the healthcare sector. As a result, our project is an attempt to solve a healthcare problem currently people are facing. The main objective of our project is to design a remote healthcare system. It covers of three main parts. The first part is, detection of patient’s condition with the proposed system, second is to storing data on cloud storage and the last part is to provide the data for remote viewing. Remote viewing of the data enables a doctor or guardian to monitor a patient’s health progress from anywhere. In this project, we have presented an IoT architecture customized for healthcare applications. The main motive of this project is to come up with a Remote Health Monitoring System that will made with locally available sensors with a view to making it affordable for everyone.