Digital Twins-Based Internet of Robotic Things for Remote Health Monitoring of COVID-19 Patients

The deadly coronavirus disease (COVID-19) has highlighted the importance of remote health monitoring (RHM). The digital twins (DTs) paradigm enables RHM by creating a virtual replica that receives data from the physical asset, representing its real-world behavior. However, DTs use passive internet of things (IoT) sensors, which limit their potential to a specific location or entity. This problem can be addressed by using the internet of robotic things (IoRT), which combines robotics and IoT, allowing the robotic things (RTs) to navigate in a particular environment and connect to IoT devices in the vicinity. Implementing DTs in IoRT, creates a virtual replica (virtual twin) that receives real-time data from the physical RT (physical twin) to mirror its status. However, DTs require a user interface for real-time interaction and visualization. Virtual reality (VR) can be used as an interface due to its natural ability to visualize and interact with DTs. This research proposes a real-time system for RHM of COVID-19 patients using the DTs-based IoRT and VR-based user interface. It also presents and evaluates robot navigation performance, which is vital for remote monitoring. The virtual twin (VT) operates the physical twin (PT) in the real environment (RE), which collects data from the patient-mounted sensors and transmits it to the control service to visualize in VR for medical examination. The system prevents direct interaction of medical staff with contaminated patients, protecting them from infection and stress. The experimental results verify the monitoring data quality (accuracy, completeness, timeliness) and high accuracy of PT’s navigation. Author

Expert Systems for Improving the Effectiveness of Remote Health Monitoring in COVID-19 Pandemic: A Critical Review

COVID-19 is an infectious disease caused by SARS-CoV-2 virus. It has disrupted the normal life of people, medical infrastructure, and economy globally. Remote health monitoring is a better option in pandemic diseases such as COVID and Ebola virus. Remote health monitoring can be enhanced by effectively using various recent advancements in technology. Technological advancements such as Wireless Body Area Networks (WBAN), Internet of Things (IoT), Artificial Intelligence (AI), and medical robotics for improving the effectiveness of remote health monitoring in COVID-19 pandemic are reviewed and presented in this chapter. Building expert systems using WBAN, IoT, AI, and robotics is an optimal choice to remote monitor COVID and reduce infection spread and mortality. Detailed architecture, use cases, impacts, workflow, applications, and future directions toward building a better expert system is highlighted in this chapter. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

On the Design of Communication-Efficient Federated Learning for Health Monitoring

With the booming deployment of Internet of Things, health monitoring applications have gradually prospered. Within the recent COVID-19 pandemic situation, interest in permanent remote health monitoring solutions has raised, targeting to reduce contact and preserve the limited medical resources. Among the technological methods to realize efficient remote health monitoring, federated learning (FL) has drawn particular attention due to its robustness in preserving data privacy. However, FL can yield to high communication costs, due to frequent transmissions between the FL server and clients. To tackle this problem, we propose in this paper a communication-efficient federated learning (CEFL) framework that involves clients clustering and transfer learning. First, we propose to group clients through the calculation of similarity factors, based on the neural networks characteristics. Then, a representative client in each cluster is selected to be the leader of the cluster. Differently from the conventional FL, our method performs FL training only among the cluster leaders. Subsequently, transfer learning is adopted by the leader to update its cluster members with the trained FL model. Finally, each member fine-tunes the received model with its own data. To further reduce the communication costs, we opt for a partial-layer FL aggregation approach. This method suggests partially updating the neural network model rather than fully. Through experiments, we show that CEFL can save up to to 98.45% in communication costs while conceding less than 3% in accuracy loss, when compared to the conventional FL. Finally, CEFL demonstrates a high accuracy for clients with small or unbalanced datasets. © 2022 IEEE.

Smart Medical Devices to Help Patients and Health Workers: A Survey

IoT(Internet of Things) devices are very useful tools for health monitoring, telehealth and teleconsultation. They bring a very positive added value to the different modes of health intervention. Health is one of the most valuable aspects of our lives. In some countries, especially in developing countries, health facilities are not sufficiently developed to meet the needs of the population for primary health care and emergency response. Therefore, there is a need to replace these gaps with IoT devices that will fill the medical deserts. For this reason, in this article, a general review of the literature on IoT devices for health is done. It results in a proposal for a new type of intelligent medical device to assist patients and health workers equipped with sensors for the automatic collection of patients' physiological data in order to make medical consultation more easily accessible to all and at a distance and, on the other hand, to alleviate the shortage of health workers and, moreover, to free doctors from the repetitive tasks they perform at each examination so that they can concentrate on the care to be administered and the psychological care of patients. © 2022 IEEE.

Activity Based Remote Health Monitoring System

Healthcare sectors are majorly moving towards Remote Health Monitoring Systems (RHMS) after the COVID-19 pandemic outbreak across the world. RHMS involves monitoring the patient's vital parameters remotely and providing advice and consultation online. Alerts are generated whenever a particular health parameter exceeds the threshold and sent to the medical officers for further actions. However, it is observed that these thresholds are applicable only when a patient is at rest and can change drastically during patient's physical activity such as walking, climbing the staircase, during exercise etc., which can mislead in understanding the patient's health condition. Hence there is a requirement to correlate these parameter values with the current activity the patient is in and to generate activity-based dynamic thresholds. In this paper, a method to correlate the sensor values with physical activities is proposed. The activity-based RHMS (aRHMS) uses the motion sensors available in the patient's smartphone to predict the activity and will automatically adjust the threshold values in co-relation with the activities and provides alarms/alerts accordingly. © 2022 IEEE.

The Use of Audio Signals for Detecting COVID-19: A Systematic Review.

A systematic review on the topic of automatic detection of COVID-19 using audio signals was performed. A total of 48 papers were obtained after screening 659 records identified in the PubMed, IEEE Xplore, Embase, and Google Scholar databases. The reviewed studies employ a mixture of open-access and self-collected datasets. Because COVID-19 has only recently been investigated, there is a limited amount of available data. Most of the data are crowdsourced, which motivated a detailed study of the various pre-processing techniques used by the reviewed studies. Although 13 of the 48 identified papers show promising results, several have been performed with small-scale datasets (<200). Among those papers, convolutional neural networks and support vector machine algorithms were the best-performing methods. The analysis of the extracted features showed that Mel-frequency cepstral coefficients and zero-crossing rate continue to be the most popular choices. Less common alternatives, such as non-linear features, have also been proven to be effective. The reported values for sensitivity range from 65.0% to 99.8% and those for accuracy from 59.0% to 99.8%.

Towards Reliable Remote Health Monitoring in Fog Computing Networks

As the World is still facing the COVID-19 pandemic, several researchers and industry players have proposed technological solutions to help fight the pandemic and pave the way for post-pandemic era precautions. In this matter, the potential benefits of remote health monitoring have been brought back to the spotlight. Indeed, with current advances in wireless communications, core network virtualization, and computing architectures as enablers, consistently guaranteeing the stringent quality-of-service (QoS) requirements of remote health monitoring, e.g., ultra-low latency, may be achievable. Notably, the fog computing (FC) paradigm has been advocated as a potential solution for remote health monitoring. However, the unreliability of fog nodes in FC networks is a critical aspect often overlooked despite its significant impact on vital latency requirements. This paper proposes a reliable fog-based remote health monitoring framework operating under uncertain fog computing conditions. Specifically, we formulate the problem of assigning tasks of remote sensors attached to patients to their adequate applications deployed in fog nodes aiming to maximize the number of satisfied tasks with respect to the fog nodes’availability and communication latency constraints. Due to the problem’s NP-hardness, we leverage a differential evolution-based algorithm enhanced by reinforcement learning to deploy applications in fog nodes. Numerical results demonstrate the superior reliability performance of our proposed solution, in terms of the average success ratio of tasks, compared to benchmarks. Specifically, our simulations show up to 60 % performance improvement compared to benchmarks in specific scenarios. Moreover, by investigating the impact of several key parameters, we identify a design trade-off between the number of fog nodes and the latter’s intrinsic failure rates. IEEE

Design of Fog Based Remote Health Monitoring System

As a result of the Covid-19 outbreak, a trustworthy health care system for remote surveillance was required, particularly in care facilitieas for the elderly. Many studies have been done in this subject, however they still have security, latency, extended time of execution and response delay. An intelligent Healthcare infrastructure termed Remote Health Monitoring (RHM) is introduced in this study to overcome these constraints. The framework uses high-level fog layer services including locally storage, native real-time data processing with combined mining of information in handling certain cloud and sensor network loads and transformed in a decision taker entity. This systems uses a body and camera sensors to diagnose, increasing accuracy and efficiency while protecting privacy. The suggested framework was tested using the iFogSim toolbox. It may minimise latency, energy usage, network connectivity and total reaction time. This work will assist develop a high performing, secure, and dependable intelligent Medical infrastructure. © 2022 IEEE.

Remote Health Monitoring using IoT and Edge Computing

The Healthcare sector has significantly developed a lot. All Nations started giving more importance to the medical field with the appearance of the new Covid19. But, some countries have been struggling with poor infrastructure in the form of insufficient medical equipment and a shortage of manpower in the medical stream. In such a situation, Remote health monitoring will lighten the burden and ease the process. Currently, smart healthcare is a combination of IoT and cloud architecture. The IoT sensors keep track of patients' health and provide clinically relevant data, which can be used for further processing. If we send all of the raw data generated by the sensors to the cloud for bulk data processing and analysis to make real-time decisions. It would impose several risks such as latency issues, bandwidth congestion, network reliability, high storage cost, and security-related issues which can negatively impact the healthcare industry on whole. To overcome the abovementioned issues, we offer 'Edge Computing, A new emerging technology that allows data processing to be done closer to the data generating device'. Our main aim is to strengthen the existing system. In this paper, the proposed system will continuously collect three main vital parameters from patients in real-time and process the collected data both on the cloud and edge architecture to compare and determine which technology is best suited for time-sensitive applications. © 2022 IEEE.

Resource Aware Fog Based Remote Health Monitoring System

In today's world of medical science, remote patient monitoring devices are becoming more important and a future need particularly in the present COVID-19 situation as individuals are preferred to be kept isolated. Patients would be benefited from a suitable monitoring system that measures their important medical parameters such as pulse rate, oxygen saturation or SpO2, body temperature, blood pressure, and Galvanized Skin Response (GSR). This system can increase the medical staff efficiency by drastically decreasing their duties in hospitals and the need to attend to them individually. Patients in their home isolation may utilize the device as well, and their vital indicators may be checked by doctors remotely. In this work, we are prototyping a powerefficient, wearable medical kit and a resource-aware fog network set up to handle the Internet of Things (IoT) data traffic. The idea behind the design is to process the critical medical sensors' data in the fog nodes which are deployed at the edge of the network. The data thus received, is used for a machine learning-based solution for personal health anomalies and COVID-19 infection risk analysis. © 2022 IEEE.

Remote Health Monitoring and Doctor on Call System

People's lives can be lost if they do not receive timely medical treatment;therefore, prompt medical care is vital. Furthermore, due to a lack of constant vital monitoring, early symptoms of major medical conditions, such as an irregular heartbeat or abnormal ECG output, are occasionally ignored. As a result, remote monitoring of the elderly and disabled is important during the COVID 19 outbreak. To solve these issues, a "Remote Health Monitoring and Doctor on Call System"has been developed. Health data exchange between healthcare providers and family members is becoming more prevalent these days. It ensures that patients' health results are safer and better. Sharing health-care data is also essential for lowering health-care expenses. It is feasible to remotely monitor a crippled or elderly patient's health. In addition to specialists, guardians can obtain a comprehensive picture of the patient's medical history. To address the issue of insufficient medical care and assistance for elderly and disabled patients, a unique and comprehensive Remote Health Monitoring and Doctor on Call System that can monitor the patient's vital signs such as heart rate, body temperature, and ECG output of the heart;monitor the patient's environment;display and store all of this information via a cloud server using ThingSpeak and Ubidots IoT platform;display these critical statistics with a mobile application for Android/iOS and in the event of an emergency or if vital signs are abnormal, contact the nearest hospital was developed in this paper. © 2022 IEEE.

Lightweight Security Framework for IoT Enabled Tracking of COVID-19 and its Variants

COVID-19 became the headlines of every nation because of its unrivalled transmission speed and outbreaks in humans, but its new variants are raising new challenges after every few months iteratively. Keeping track of travel history and maintaining a record of people coming in contact with any COVID affected individual has become a prime concern to control the spread of this pandemic disease. The existing tracking system lags in fetching records of people who came in contact with the affected one and maintaining travel history across the local boundaries of the cities. In this position paper, we are proposing a framework for an IoT driven blockchain (BC) based secured tracking system that gathers users' travel and meeting history, and it may help with remote health monitoring. The data gathered for the same is treated as immutable and achieves interoperability with the help of Smart Contracts (SC). It could be proved as a useful framework for post COVID-19 economic revival with the help of an IoT driven blockchain based secured model for remote health monitoring & chain tracking. © 2021 IEEE.

Remote monitoring of chronic noncommunicable diseases: potential in the COVID-19 pandemic

Aim. To review the current progress in the use of remote health monitoring (RHM) technologies for chronic noncommunicable diseases (CNCD). To search for data, we used Web of Science, Scopus, Russian Science Citation Index, Academic Search Complete (EBSCO), Cochrain, and PubMed databases. The date range was 5-10 years. The importance of development of RHM technologies and their further study was shown to confirm the evidence of effect of certain RHM systems. New approaches to the integration of the medical community into the international telemedicine strategy are considered. It was established that RHM can potentially decrease treatment costs and reduce the burden on medical organizations. The review analyzes the experience in using RHM in patients with cardiovascular diseases, as well as respiratory and endocrine disorders. The review also summarizes and systematizes the findings of studies on assessing the effectiveness of RHM technologies in clinical practice, including their use in the COVID-19 pandemic. It is noted that despite high interest of the scientific community in the study of RHM technologies, unambiguous results demonstrating the effectiveness of such developments in clinical practice have not been presented.

IRHMP: IoT-Based Remote Health Monitoring and Prescriber System

The Novel Corona Virus Disease-2019 (COVID-19), which created this pandemic, makes us realize the importance of universal social and health care systems. The frontline workers worked restlessly during the pandemic and few of them also lost their lives. There is a need for a remote IoT health monitoring system that takes care of the health of infected patients, conducts regular health checks, and reduces contact between an infected person and health workers. This especially helps the patients with mild symptoms who are home quarantined. The IoT system monitors a person 24/7 and a report can be generated and sent to the doctor at the same time. However, such a procedure will produce a large amount of data. A major research challenge addressed in this paper is to effectively transfer health care data with the help of existing network infrastructure and transfer it to the cloud. In this paper, we have identified the key network and infrastructure requirements for a standard health monitoring system based on real-time event updates, bandwidth requirements, data collection, and data analysis. After that, we propose IRHMP- IoT-based remote healthcare device that delivers health care data efficiently to the cloud and the web portal. Finally, we have proposed a machine-learning algorithm to provide and predict future health risks with the help of recorded data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Reliability os SmartPhone Applications for Remote Health Management in the Context of COVID-19

The unfolding of the Covid 19 Global Crisis directly impacted healthcare organizations around the world, leveraging technological innovations focused on care and remote medical monitoring, being important tools for crisis management. This study aims to determine the existence of a causal relationship between the level of education and users’ trust in smartphone health applications in the context of Covid 19. From the triangulation of primary data, the literature review, and the survey of secondary statistical data, it was possible to realize that there is no positive relationship between the level of education of smartphone users and the reliability imposed on mobile solutions made available by public and private agencies, as well as collaborative devices, which encourages the characterization of the potentiality and accessibility of this instrument for remote health management in times of sanitary restrictions on social contact. © 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Design and Development of Cloud Based Mobile Health Monitoring System

The present covid-19 pandemic necessitates the need for a cost effective and efficient remote health monitoring mechanisms for the collection, transmission, evaluation, and communication of patient health data from electronic devices since many find it difficult to go for regular medical checkup due to safety concerns. Therefore, we require a remote health monitoring system that can monitor different body parameters and transmit it to a health care provider in a remote location. This paper describes the design and development of a remote health monitoring system capable of measuring ECG, Heart rate and SpO2 using AD8232, MAX30100 or MAX30102 and transfer this information to physicians using IoT. The ESP32 module is used to establish the IoT connectivity and share the vital information to medical practitioners for real-time analysis through a smartphone or Tab. In the present work, Blynk IoT cloud platform is used to communicate with hardware and software. The size of the designed PCB board is less than 60mm x 60 mm, therefore the size and weight of the device are very small and able to attach on the patient's cloth or in the body with the help of a Velcro belt. This helps the patient to easily carry the device without any discomfort. © 2021 IEEE.

Architecture Design of AI and IoT based System to Identify COVID-19 Protocol Violators in Public Places

The World Health Organization (WHO) describes COVID-19 as a pandemic that is causing a worldwide health disaster. Wearing a face mask in public places is the most effective method to curb the spread of the virus. The Internet of Things is emerging as one of the most significant innovations and playing a vital role during the pandemic. Affordable remote health monitoring devices help doctors to track quarantined patients. Our government is trying its best to control the spread of the virus. Citizens who do not follow the protocols serve as the reason for these widespread infections. Our work proposes a system to identify protocol violators in real-time. Our system consists of a face mask detection module, a social distance monitoring module, and a non-contact temperature monitoring module. We intend to deploy our proposed approach in public places such as airports, schools, and hospitals. These modules depend on video feeds from general security cameras and IoT sensor nodes deployed around public areas. Health care and security officials can subscribe to real-time data feeds to track public behaviour. © 2021 IEEE.

Android based Cloud Platform for Health Monitoring of COVID19 Affected Person

Healthcare is a human right that must be accessible to all disregarding the social or economic conditions of any human being. The burden on healthcare system has increased immensely in the last few months. The COVID19 pandemic has brought to the fore gaps in the healthcare system world over. The doctors and front-line workers are directly getting exposed to the virus and patients that might need other healthcare services are vulnerable to the exposure. These problems would be catered by the proposed device as it would be operated by the patients and the real time data can be collected by the doctors to assess the vital body parameters through cloud without being physically present in the same environment. The parameters that can be monitored are body temperature, pulse rate, and oxygen saturation level. Hence, the proposed device includes sensors for measuring the body temperature (i.e. LM35) and pulse and oxygen level (i.e. MAX30100). The experimental setup has been built using Android based Blynk Cloud Platform where data is collected from remote places and stored the cloud. It is further available for assessment by the healthcare professionals. © 2021 IEEE.

Towards Improving Patient Health Monitoring System using Machine Learning and Internet of Things

In today's world, everyone's health is a major concern and a top priority. Humans are afflicted with a plethora of diseases because of their unhealthy habits. People are primarily affected by heart attacks and low oxygen levels because of poor medical care and late diagnosis. As a result, this work aims to combat such untimely deaths using smart health monitoring, which employs machine learning and IoT. The proposed system includes ThingSpeak cloud to communicate with the doctor in case of any emergency. This system consists of body temperature sensor, pulse oximeter sensor (for collecting heartbeat rate and oxygen level) and blood pressure sensing module for tracking patient's health. These sensors are interfaced with the Raspberry pi and Arduino Uno microcontroller. The obtained result from patients is continuously monitored and it is updated in LCD and doctor's webpage using Internet of Things. Following these steps, a trained Machine Learning model is used to determine the type of disease being experienced by the patient. This system predicts Normal and two major disease namely Hypertension and Lung disease. By incorporating all these features, we can ensure that people who suffer from heart attacks and lung disease will not die suddenly. The accuracy of this proposed method is 86% approximately in a real time scenario. Furthermore, because raw medical data can be analyzed in a short period of time, the work will aid clinicians in remote monitoring during epidemic situations such as covid. © 2021 IEEE.

Soft Transducer for Patient's Vitals Telemonitoring with Deep Learning-Based Personalized Anomaly Detection.

This work addresses the design, development and implementation of a 4.0-based wearable soft transducer for patient-centered vitals telemonitoring. In particular, first, the soft transducer measures hypertension-related vitals (heart rate, oxygen saturation and systolic/diastolic pressure) and sends the data to a remote database (which can be easily consulted both by the patient and the physician). In addition to this, a dedicated deep learning algorithm, based on a Long-Short-Term-Memory Autoencoder, was designed, implemented and tested for providing an alert when the patient's vitals exceed certain thresholds, which are automatically personalized for the specific patient. Furthermore, a mobile application (EcO2u) was developed to manage the entire data flow and facilitate the data fruition; this application also implements an innovative face-detection algorithm that ensures the identity of the patient. The robustness of the proposed soft transducer was validated experimentally on five individuals, who used the system for 30 days. The experimental results demonstrated an accuracy in anomaly detection greater than 93%, with a true positive rate of more than 94%.