Remote Patient Health Monitoring Research Articles

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

Model for recognizing human behavior via feature and classifier selection

Motion or inertial sensors, like the accelerometer and gyroscope that are frequently found in smartphones and smartwatches, can measure the acceleration and angular velocity of bodily movements and be used to teach bots so that they may guess human activities. These models can be selected to a variety of fields, including biometrics and remote patient health monitoring. Because deep learning-based methods employ representing teaching methods, those may automatically identify hidden patterns in data and generate optimal objects from basic information generated from sensors without human intervention, they got popular in earlier in recognizing human activities. Along with recognize human activity, this paper suggests a novel called HDDN-model called CNN-GRU, which combines convolutional units. This model exhibited accuracy that is suggestively better than other state-of-the-art DNN models like Inception Time and Deep Conv LSTM developed using Auto ML, and was successfully verified on the WISDM dataset.

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.

Hash message authentication codes for securing data in wireless body area networks

SummaryThere are numerous medical applications for the growing use of wireless body area networks (WBANs), including remote patient health monitoring, early illness detection, and computer‐assisted rehabilitation. WBAN links many sensor nodes implanted or affixed to the human body to monitor physiological data. WBAN technology has the potential to benefit medical healthcare systems tremendously. However, the gathering and transferring sensitive physiological data in an unprotected environment raises severe security and privacy concerns. The limited resources and broadcast transmission of a WBAN pose grave safety issues in biomedical applications. Keeping sensitive patient data safe during broadcasts is critical in the healthcare business. As a result of the massive memory and processing requirements required by traditional public or private key architectures, tiny sensor nodes cannot use them. WBAN sensor nodes can communicate securely using the KHMAC key‐agreement technique proposed in this article. Measurements and confirmations of shared physiological parameters at the transmitter and recipient sensors are key to the proposed protocol KHMAC before communication is established. The proposed KHMAC protocol enables sensors to use their prior session knowledge for secure communication within a predetermined time window. This will shorten the time it takes to establish a shared key, prevent the retransmission of extracted characteristics in the medium and eavesdropping attacks, and preserve the unpredictability of the key. Both the feature extraction and key agreement stages will be shown to have higher precision and lower error rates with KHMAC's proposed key management methodology. The proposed protocol is proven to be more energy and memory efficient than existing key agreement systems.

Intelligent Healthcare: Integration of Emerging Technologies and Internet of Things for Humanity.

Health is gold, and good health is a matter of survival for humanity. The development of the healthcare industry aligns with the development of humans throughout history. Nowadays, along with the strong growth of science and technology, the medical domain in general and the healthcare industry have achieved many breakthroughs, such as remote medical examination and treatment applications, pandemic prediction, and remote patient health monitoring. The advent of 5th generation communication networks in the early 2020s led to the Internet of Things concept. Moreover, the 6th generation communication networks (so-called 6G) expected to launch in 2030 will be the next revolution of the IoT era, and will include autonomous IoT systems and form a series of endogenous intelligent applications that serve humanity. One of the domains that receives the most attention is smart healthcare. In this study, we conduct a comprehensive survey of IoT-based technologies and solutions in the medical field. Then, we propose an all-in-one computing architecture for real-time IoHT applications and present possible solutions to achieving the proposed architecture. Finally, we discuss challenges, open issues, and future research directions. We hope that the results of this study will serve as essential guidelines for further research in the human healthcare domain.

Implementation of Telemedicine through “Apoteker Keluarga Online” Application as an Effort for Rational Headache Self-Medication

Implementation of Telemedicine through “Apoteker Keluarga Online” Application as an Effort for Rational Headache Self-Medication

Security paradigm for remote health monitoring edge devices in internet of things

The tremendous growth in the internet of things (IoT) technology has provided great support for e-healthcare applications. The remote health monitoring of patients at home is gaining popularity with the increase in design of number of IoT enabled wearable devices. The secure and efficient health data retrieval during continuous uploading of huge data on network is a challenge. In addition, another challenge exists due to limited resources available with IoT device to proceed efficient data retrieval. On the other hand, the edge cloud in edge computing technology provide abundant resources to its devices for various computations, storage and processing. The integration of IoT with edge would solve the resource constraint issue. However, efficient retrieval of health data from large pool of data would still be an issue due to IP based solutions offered by IoT. The Named Data Networking (NDN) is a promising solution for efficient delivery of data based on its name based content searching. This paper proposed NDN communication based secure remote healthcare monitoring framework by integrating IoT with edge computing where security to the each individual content is provided through hashing and encryption mechanism. The performance of the proposed framework is evaluated against its companion in Icarus simulator. The results proved the efficiency of our framework for content retrieval delay and cost associated in accessing the required data.

Remote Patient Health Monitoring System

Abstract: With 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 database 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. With 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. Keywords: IoT, MCU, Oxygen Level, Humidity

Design and Implementation of Wearable Patient Health Monitoring System Using WBAN and IoT

A remote patient health monitoring system using Wireless Body Area Network (WBAN) has been proposed to detect sudden possible falls in elderly people, acquire body temperature and ECG (Electrocardiogram) via wireless sensor nodes viz an accelerometer, a temperature sensor, and 3 bipolar limb leads. These nodes carry information to a coordination node with the freedom of unconstrained movement, by allowing the nodes to interact with the host node seamlessly. Leads 2 and 3 gives an inferior view of the heart to detect a probable heart attack or MI that might occur in the inferior region. Dynamic Time Warping (DTW) has been employed in this work for the detection of ST elevation, T inversion, and deep Q on lead 2 data. Atrial fibrillation, the most common kind of arrhythmia, is anticipated from the acquired lead 2 data using the standard deviation parameter. The 3 lead ECG report is then forwarded to the doctor via email for remote monitoring and diagnostic purposes.

Traffic Priority-Aware Medical Data Dissemination Scheme for IoT Based WBASN Healthcare Applications

Wireless Body Area Sensor Network (WBASN) is an automated system for remote health monitoring of patients. WBASN under umbrella of Internet of Things (IoT) is comprised of small Biomedical Sensor Nodes (BSNs) that can communicate with each other without human involvement. These BSNs can be placed on human body or inside the skin of the patients to regularly monitor their vital signs. The BSNs generate critical data as it is related to patient's health. The data traffic can be classified as Sensitive Data (SD) and Non-sensitive Data (ND) packets based on the value of vital signs. These data packets have different priority to deliver. The ND packets may tolerate some delay or packet loss whereas, the SD packets required to be delivered on time with minimized packet loss otherwise it can be life threating to the patients. In this research, we propose a Traffic Priority-aware Medical Data Dissemination (TPMD²) scheme for WBASN to deliver the data packets according to their priority based on the sensitivity of the data. The assessment of the proposed scheme is carried out in various experiments. The simulation results of the TPMD² scheme indicate a significant improvement in packets delivery, transmission delay and energy efficiency in comparison with the existing schemes.

Design a Remote Patient Health Monitoring System (RPHMS) Using IoT

Healthcare has become one of the principal issue with the rise in human population and medical expenditure. For a healthy life, it is essential to follow human body’s vital signals. Continuous Monitoring of patient’s vital signals cannot be provided outside hospital. As it is hard to monitor the patient’s condition for 24 hours, it was proposed in this paper to observe continuously the condition of patient despite the patient being busy with his routine and to screen the health status to the doctors through Internet of Things. This paper proposes health monitoring system using non-intrusive biomedical sensors that measure five parameters like ECG, SPO2, toxic gas, temperature and blood pressure. Proposed method makes use of Arduino Uno Controller to which non-invasive biomedical sensors are connected. The output is displayed on any digital monitoring system using Arduino Uno. The data obtained from the sensors is uploaded to the Thing Speak cloud to store and to access patient’s information by their doctors or by the concerned for necessary follow-ups in real-time. IoT is a powerful domain where sensors can connect and data is viewed over the Internet.

Internet of Medical Things-Based Secure and Energy-Efficient Framework for Health Care.

The Internet of Medical Things (IoMT) is a collection of medical equipment and software that can help patients get better care. The purpose of this study is to improve the security of data collected through remote health monitoring of patients utilizing Constrained Application Protocol (CoAP). Asymmetric cryptography techniques may be used to assure the security of such sensor networks. For communication between different IoMT devices and a remote server, the safe CoAP is compatible with the Datagram Transport Layer Security (DTLS) protocol for creating a secure session using existing algorithms such as Lightweight Establishment of Secure Session. The DTLS layer of CoAP, in contrast, has shortcomings in key control, session establishment, and multicast message exchange. As a consequence, for IoMT communication, the creation of an efficient protocol for safe CoAP session establishment is needed. Thus, to solve the existing problems related to key management and multicast security in CoAP, we have proposed an efficient and secure communication technique to establish a secure session key between IoMT devices and distant servers using lightweight Energy-Efficient and Secure CoAP Elliptic Curve Cryptography (E2SCEC2). The advantage of using E2SCEC2 over other identification methods such as Rivest-Shamir-Adleman (RSA) is its compact key size, which allows it to use a smaller key size. This article also compares these algorithms on parameters such as time spent generating keys, signature generation, and verification of E2SCEC2 and RSA algorithms, as well as energy consumption and radio duty cycle, to see if they are compatible in constrained environments.

Deep learning based human activity recognition (HAR) using wearable sensor data

Motion or inertial sensors such as gyroscope and accelerometer commonly found in smartwatches and smartphones can measure characteristics such as acceleration and angular velocity of movements in the human body and use them to learn models capable of identifying human activities, that has applicability in various fields such as biometrics, remote patient health monitoring, etc. Recently deep learning-based methods have become popular for human activity recognition because they use representation learning techniques that can automatically generate optimal features from raw input data generated from sensors without any human intervention and can identify hidden patterns in data. This work proposes a novel hybrid deep neural network model, CNN-GRU that combines convolutional and gated recurrent units for human activity recognition. This model was successfully validated on WISDM dataset and produced accuracy that is suggestively better than other state-of-the-art deep neural network models such as Inception Time and DeepConvLSTM created using AutoML.

Edge device based efficient data collection in smart health monitoring system using wireless body area network

Wireless Body Area Network (WBAN) provides continuous remote patient health monitoring without disrupting their normal daily activities. In WBAN, the bio-sensor is placed on the human body to detect vital changes due to the abnormal behavior of human organs and send the sensed data via the Internet to the medical/cloud server. However, due to the processing of huge amounts of data generated by the EEG and ECG sensor, resource-limited networks face a big challenge in satisfying the required Quality of Services (QoS). Moreover, the WBAN often meets the major problem of QoS degradation due to congestion. In this paper, Minimum Edge-shared Vertex Path Selection (MEVPS) for WBAN seeks to mitigate the congestion problem and decreases the latency during emergency packets delivery. A minimum edge-shared vertex path between the source and the Base Station (BS) is created by updating neighbor information using Ant Colony Algorithm (ACA). In addition, the body controller in the proposed system minimizes the data traffic entered into the network through the use of packet discard policy. It is observed from the simulation results that the proposed routing strategy demonstrates a better efficacy of QoS parameters.

Wearable IoTs and Geo-Fencing Based Framework for COVID-19 Remote Patient Health Monitoring and Quarantine Management to Control the Pandemic

The epidemic disease of Severe Acute Respiratory Syndrome (SARS) called COVID-19 has become a more frequently active disease. Managing and monitoring COVID-19 patients is still a challenging issue for advanced technologies. The first and foremost critical issue in COVID-19 is to diagnose it timely and cut off the chain of transmission by isolating the susceptible and patients. COVID-19 spreads through close interaction and contact with an infected person. It has affected the entire world, and every country is facing the challenges of having adequate medical facilities along with the availability of medical staff in rural and urban areas that have a high number of patients due to the pandemic. Due to the invasive method of treatment, SARS-COVID is spreading swiftly. In this paper, we propose an intelligent health monitoring framework using wearable Internet of Things (IoT) and Geo-fencing for COVID-19 susceptible and patient monitoring, and isolation and quarantine management to control the pandemic. The proposed system consists of four layers, and each layer has different functionality: a wearable sensors layer, IoT gateway layer, cloud server layer, and client application layer for visualization and analysis. The wearable sensors layer consists of wearable biomedical and GPS sensors for physiological parameters, and GPS and Wi-Fi Received Signal Strength Indicator acquisition for health monitoring and user Geo-fencing. The IoT gateway layer provides a Bluetooth and Wi-Fi based wireless body area network and IoT environment for data transmission anytime and anywhere. Cloud servers use Raspberry Pi and ThingSpeak cloud for data analysis and web-based application layers for remote monitoring based on user consent. The susceptible and patient conditions, real-time sensor’s data, and Geo-fencing enables minimizing the spread through close interaction. The results show the effectiveness of the proposed framework.

Trust Enforced Computational Offloading for Health Care Applications in Fog Computing

Internet of Things (IoT) is a network of internet connected devices that generates huge amount of data every day. The usage of IoT devices such as smart wearables, smart phones, smart cities are increasing in the linear scale. Health care is one of the primary applications today that uses IoT devices. Data generated in this application may need computation, storage and data analytics operations which requires resourceful environment for remote patient health monitoring. The data related with health care applications are primarily private and should be readily available to the users. Enforcing these two constraints in cloud environment is a hard task. Fog computing is an emergent architecture for providing computation, storage, control and network services within user’s proximity. To handle private data, the processing elements should be trustable entities in Fog environment. In this paper we propose novel Trust Enforced computation ofFLoading technique for trust worthy applications using fOg computiNg (TEFLON). The proposed system comprises of two algorithms namely optimal service offloader and trust assessment for addressing security and trust issues with reduced response time. And the simulation results show that proposed TEFLON framework improves success rate of fog collaboration with reduced average latency for delay sensitive applications and ensures trust for trustworthy applications.

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.

Development of a wearable device for the early diagnosis of neurodegenerative diseases

The progressive evolution of information and sensing technologies is giving pulse to the development of wearable mobile devices in search of life quality improvement. A relevant field of application is healthcare, with the development of wireless unobtrusive wearable solutions for the continuous remote health monitoring of patients. These wearable devices are particularly important for neurodegenerative diseases due to the possibility of early stage diagnoses through continuous monitoring to collect earlier significant data. Discovering specific symptoms and early defining medical treatments can delay, if not stop, the pathology progress whereas, once major symptoms like restricted or impaired mobility has appeared, the patients already underwent relevant and irreversible brain damage. The aim of this work is to show the development of the Neuroglass, a wearable smart glasses device for early stage diagnosis and monitoring of Parkinsonian-type neurodegenerative diseases. The designed frame is compliant to the standards and in order to embed the sensors to collect data from head and eyes movements since one of the early symptoms of Parkinson’s disease has proven to be eye tremors. Preliminary laboratory tests, e.g. head accelerations measurements for different body movements, were carried out in order to choose properly the characteristics and positioning of the sensors; afterwards the device’s frame was designed by means of a 3D parametric CAD and built by additive manufacturing. The design was validated by first experimental test on monitoring eye movements and blinks.

Platform for Healthcare Promotion and Cardiovascular Disease Prevention.

This article presents the hardware-software design and implementation of an open, integrated, and scalable healthcare platform oriented to multiple point-care scenarios for healthcare promotion and cardiovascular disease prevention. The platform has the capability to provide continuous monitoring, extended device integration, strategies based on artificial intelligence for the information analysis and cybersecurity support, delivering a secure end-to-end hardware-software solution. This platform is used to perform the remote patient health monitoring and supervision by doctors, triage procedures in hospitals, or self-care monitoring using personal devices such as tablets and cellphones. The proposed hardware architecture facilitates the integration of biomedical data acquired from different health-point cares, collecting relevant information for the detection of cardiovascular risk through deep-learning algorithms. All these characteristics make our development a strong tool to perform epidemiological profiling and future implementation of strategies for comprehensive cardiovascular risk intervention. The components of the platform are described, and their main functionalities are highlighted.

IOT and Cloud Based Remote Patient Health Monitoring System

IOT and Cloud Based Remote Patient Health Monitoring System