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Moved by the necessity, also related to the ongoing COVID-19 pandemic, of the design of innovative solutions in the context of digital health, and digital medicine, Wireless Body Area Networks (WBANs) are more and more emerging as a central system for the implementation of solutions for well-being and healthcare. In fact, by elaborating the data collected by a WBAN, advanced classification models can accurately extract health-related parameters, thus allowing, as examples, the implementations of applications for fitness tracking, monitoring of vital signs, diagnosis, and analysis of the evolution of diseases, and, in general, monitoring of human activities and behaviours. Unfortunately, commercially available WBANs present some technological and economic drawbacks from the point of view, respectively, of data fusion and labelling, and cost of the adopted devices. To overcome existing issues, in this paper, we present the architecture of a low-cost WBAN, which is built upon accessible off-the-shelf wearable devices and an Android application. Then, we report its technical evaluation concerning resource consumption. Finally, we demonstrate its versatility and accuracy in both medical and well-being application scenarios. Author
ABSTRACT
The outbreak of the coronavirus disease 2019 (COVID-19) has become the worst public health event in the whole world, threatening the physical and mental health of hundreds of millions of people. However, because of the high survivability of the virus, it is impossible for humans to eliminate viruses completely. For this reason, it is particularly important to strengthen the prevention of the transmission of viruses and monitor the physical status of the crowd. Wireless sensors are a key player in the fight against the current global outbreak of the Covid-19 pandemic, where they are playing an important role in monitoring human health. The Wireless Body Area Network (WBAN) composed of these wireless sensor devices can monitor human health data without interference for a long time, and update the data in almost real time through the Internet of Things (IoT). However, because the data monitored by the devices is relatively large and the transmission distance is long, only transmitting the data to medical centers through the personal devices (PB) cannot get feedback in time. We propose a non-cooperative game-based server placement method, which is named ESP-19 to improve the efficiency of transmission data of wireless sensors. In this paper, experimental tests are conducted based on the distribution of Shanghai Telecom's base stations, and then the performance of ESP-19 is evaluated. The results show that the proposed method in this paper outperforms the comparison method in terms of service delay. © 2022 IEEE.
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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.
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In recent decades, many infectious diseases have appeared that have negatively affected life in general and people in particular, causing many economic and human losses. Recently, many attempts have emerged to confront these diseases using computer-based technology for diagnosis, prediction, and data analysis using various techniques, the most important of which is deep learning. Previous research relied primarily on a set of images taken from the patient's body while he was in a healthcare facility, and this is the main weakness of these studies. Not all people go to a doctor or hospital when they feel the symptoms of a disease. Hence, people moving in crowded places without knowing their health status can contribute to spreading infectious diseases quickly, and this is the issue that should be confronted. Therefore, this paper presents a people-monitoring scheme, which is based on the internet of things (IoT) technology, to predict infectious disease symptoms through people's behavior as well as through a wireless body area network (WBAN). This scheme can predict the spread of disease by tracking the movements of infected persons. Additionally, a simple methodology for processing the data extracted from the monitoring process across a range of different computing centers is introduced. Moreover, to ensure the monitoring scheme operates in real-time, it was necessary to provide a powerful coverage model for its objects. Also, a simple COVID-19 case study is presented. Finally, the performance of the prediction model is measured using images, sounds and videos files. Furthermore, the performance of the data computing and coverage methodologies is measured using an intensive simulation environment for the IoT that was constructed using NS3 package. The results showed that the proposed scheme is able to predict the symptoms of disease and its spread with accepted level of accuracy. In addition, using a mixture of coverage tools and computing techniques is recommended. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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The IoT has been a subclass of Industry 4.0 standards that is under research from the perspective of quality of service (QoS) & security. Due to the pandemic situations like novel coronavirus smart healthcare monitoring gained growing interest in detection. In IoT data is communicated from Intra WBAN (Wireless Body Area Network) to inter-WBAN and then beyond WBAN. While transferring data from one layer to the other end-to-end data privacy is the challenge to focus on. The privacy-preserving of patients' sensitive data is difficult due to their open nature and resource-constrained sensor nodes. The proposed research design based on routing protocols achieves the patient's sensitive data privacy preservation along with minimum computation efforts and energy consumption. The proposed model is Secure Communication-Elliptic Curve Cryptography (SCECC) WBAN-assisted networks in presence of attackers is evaluated using NS2. The proposed privacy preservation algorithm uses efficient cryptographic solutions using hash, digital signature, and the optimization of the network. © 2022 The Korean Society for Vascular Surgery.
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This paper proposes an idea of Wireless Body Area Networks (WBANs) based on Bluetooth Low-Energy (BLE) standards to recognize and alarm a gesture of touching the face, and in effect, to prevent self-inoculation of respiratory viral diseases, such as COVID-19 or influenza A, B, or C. The proposed network comprises wireless modules placed in bracelets and a necklace. It relies on the received signal strength indicator (RSSI) measurements between the bracelet and necklace modules. The measured signal is cleared of noise using the exponential moving average (EMA). Next, we use a classification algorithm based on a Least-Squares Support Vector Machine (LSSVM) in order to detect facial touches. When the results of the classification indicate that the hand is moving toward the face, an alarm is sent through the neck module and the vibrator embedded in the wrist module is switched on. Based on the performed tests, it can be concluded that the proposed solution is characterized by high accuracy and reliability. It should be useful, especially for individuals who are regularly exposed to the risk of respiratory infections.
Subject(s)
COVID-19 , Influenza, Human , Humans , COVID-19/prevention & control , Reproducibility of Results , Upper Extremity , AlgorithmsABSTRACT
In this COVID-19 pandemic situation, health care is on the priority of every human being. The recent development in the miniaturization of intelligent devices has opened many opportunities and played a crucial role in the healthcare industry. The amalgamation of wireless sensor network and Internet of Things is the best example of wireless body area network. These tiny sensor devices have two essential evaluation parameters named as energy efficiency and stability while performing in a group. This paper focuses on various issues of the healthcare system and their solutions. An energy-efficient routing protocol that can provide sensed data to the collection centre or data hub for further processing and treatment of the patients is proposed. Here, we fixed zones for sending data to zone head using distance aware routing, and then zone head send the aggregated data to the data hub. It is better than the low energy adaptive clustering hierarchy (LEACH) by 42% and distance-based residual energy-efficient protocol (DREEP) by 30% in energy efficiency and stability 58% more by LEACH and 39% by DREEP. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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This work demonstrates a remote health monitoring system that provides a holistic perspective of cases and their health conditions. Remote Patient Monitoring (RPM) systems will play a conspicuous role in the millennium of medical management. In this paper, to monitor covid patients during their quarantine days to keep track of chronic circumstances. For that, the model of a non-reactive preference grading independently in a single device to collect the essential parameters like blood Oxygen level, temperature and pulse rate. To predict and conduct the priority division using supervised machine learning algorithm for the received medical packets and relay them according to their priorities. This hitch results in transmitting advanced significance data packets of high importance in an advanced average waiting time. In this design, to acknowledge a vital criterion distinguishing the priority of health-info carried by a file and other low-ranking digital data parcels of different cases. The stored data then given for the supervised machine learning classification algorithms. In that the better accuracy of priority classification of 93.5% obtained from support vector machine (SVM) algorithm outperforms than the other machine learning classifiers and are 91%, 88%, 89% with respect to Multilayer Perception(MLP), Baysian Network (BN) and Logisitic Regression(LR). © 2022 IEEE.
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At present, COVID-19 is still spreading and affecting millions of people worldwide. Minimizing the need for travel can significantly reduce the probability of infection and improve patients’quality of life. The wireless body area network (WBAN) transmits the patients’physiological data to the doctor remotely through the sensors in a way that minimizes physical contact with others. However, existing WBAN security authentication schemes have core limitation that includes weak authentication performance and over-consumption of resources that precludes their widespread adoption in practical applications. Therefore, in this paper, an enhanced dual-factor authentication system that address the mentioned drawbacks is proposed for securing WBAN resources. By combining iris and electrocardiogram (ECG) features, users would be required to pass the first-level iris authentication before performing the second-level ECG authentication, thus enhancing the overall security scheme of a WBAN system. Furthermore, we examined the existing Inter-Pulse-Intervals (IPI) encoding methods and propose a more efficient ECG IPI encoding algorithm which can effectively shorten the encoding time without affecting the overall encoding performance. Finally, extensive experiments were performed to verify the performance of the proposed dual-factor iris and ECG based WBAN authentication system using public iris and ECG databases. The experimental results show that the false acceptance rate (FAR) is close to 0.0% and the false rejection rate (FRR) is close to 3.2%. Findings from this study suggest that the proposed dual-factor authentication scheme could aid adequate deployment of security schemes to protect WBAN resources in practical applications. IEEE
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Wireless Body Area Network (WBAN) is a wireless sensor network composed of sensors implanted under the skin or wearable sensors. These sensors are small and battery powered, making power efficiency an important and critical consideration. Data transmission is one of the most power consuming functions in the sensor node. This paper analyzes reducing data transmission, and hence power consumption, by predicting vital signs data instead of transmitting them all the time. We have focused on predicting the body vital signs like the temperature from other vital signs like the heart rate and the respiration rate. It is shown that the percentage of energy reduction depends on the rate of the prediction. Also, sending critical data in the alternating modes consumes more energy compared with the critical and the alternative prediction modes. It is shown that the critical alternating and critical transmission modes consumes more energy in Covid-19 patient compared to healthy person with MAE does not exceed 0.24. Finally, the multivariant model shows a great advantage in accuracy over univariant model. © 2022 IEEE.
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The growth of the world's population, especially that of the elderly, along with the outbreak of infectious diseases such as COVID-19 have caused hospitals and healthcare centres to become full, and even economical treatments cost a lot. On that account, the conjunction of wireless body area networks (WBAN) and Internet of Things (IoT) for healthcare and medical diagnosis has become really important, and is accordingly one of the most popular and attractive areas of the Internet of Things (IoT). In such an IoT, a wireless body area network (WBAN) consists of a miniature sample of the Internet of Medical Things (IoMT) that can be either implanted in the human body or wearable. Nowadays, IoT has made healthcare evaluation possible. Instead of the patient being constantly hospitalized for treatment, the condition of the person is sent to the health centre by the IoMT over the Internet. IoT enables wireless communication between smart devices on one side and almost anything on the other. Since this network deals with medical and critical conditions, data must be sent to a physician or practitioner in the prescribed period;this indicates that routing is one of the most critical issues. Thus, routing is considered a very important challenge in WBANs. The present study describes thermal (temperature)-aware routing protocols in WBANs. Routing protocols in WBANs are divided into thermal (temperature)-aware, QoS-aware, security-aware, cluster-based, cross-layered, postured-based, cost-effect, link-aware, and opportunistic ones. In a WBAN, temperature rise in implant nodes can damage body tissues, which is dangerous for the patient. Accordingly, here, those algorithms were considered which are presented in thermal (temperature)-aware protocols. This paper first introduces IoT-based WBANs, their routing mechanism and challenges, after which it provides a detailed description of thermal (temperature)-aware algorithms. Finally, the advantages and disadvantages of these algorithms are presented. © 2022 The Authors. IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
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The emerging technologies in healthcare industries are adopting necessary needs for the treatment of dreadful diseases and clinical diagnoses. At the age of the COVID-19 pandemic where transmission of infection is at severe risk, the treatment of the patient can be done remotely. [1] The progression in the field of healthcare is eminently supported by involving real-time diagnosis and e-treatment of the patients. Digital technologies involved in this diagnosis are Artificial Intelligence, Big Data, Machine Learning, the Internet of Things (IoT), etc. Wireless Body Area Networks (WBANs) are the technology that has accomplished the capability to restructure healthcare and make available pervasive health care support to the patients. Enormous research is ongoing in this area and creating new ideas and technologies to make lives easy, safe, and convenient for the patients and healthcare providers. © 2022 IEEE.
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The rise of antenna technology, smartphones, and the Internet-of-things (IoT) has enabled wearable antennas for wireless communication between implantable devices such as pacemakers, infusion pumps, etc., and external devices for health monitoring. This work describes the key challenges that need to be addressed for such wireless body area network (WBAN) technologies to be integrated into large-scale health monitoring programs. These include the miniaturization of antennas, fabrication techniques to enable mass production, and methods to protect patients from data infringement and hackers. Furthermore, the role of wearable and implantable antennas is pivotal to realize devices for continuous healthcare monitoring especially during Pandemic situations such as Coronavirus Disease-2019 (COVID-19). © 2021 IEEE.
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Use of electronic devices has increased many times in our daily life. It is used for many purposes including healthcare. Small sensor devices on patients’ body that reads patients’ physiological data and send those data to a remote server. Doctors and other healthcare professionals can view this data sitting at their home. Thus remote health monitoring is possible known as Wireless Body Area Networks (WBAN). Routing and providing seamless connectivity is a big challenge and a topic of research. In this work, a priority based routing protocol designed for WBAN has been developed where data has been classified into normal and emergency data. This routing protocol is especially applicable for COVID and diabetic patients. Normal data will be processed in cloud server but emergency data will be processed locally. Results obtained prove that our protocol is faster and also gives minimum delay. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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This article presents the design of a low-cost Wireless Body Sensor Network (WBSN) for monitoring vital signs including a low-cost smart wristwatch that contains an ESP-32 microcontroller and three sensors: heart rate (HR), blood pressure (BP) and body temperature (BT), and an Internet of Things (IoT) platform. The vital signs data are processed and displayed on an OLED screen of the patient's wristwatch and sent the data over a wireless connection (Wi-Fi) and a Cloud Thing Board system, to store and manage the data in a data center. The data can be analyzed and notified to medical staff when abnormal signals are received from the sensors based on a set parameters from specialists. The proposed low-cost system can be used in a wide range of applications including field hospitals for asymptotic or mild-condition COVID-19 patients as the system can be used to screen those patients out of symptomatic patients who require more costly facilities in a hospital with considerably low expense and installation time, also suitable for bedridden patients, palliative care patients, etc. Testing experiments of a 60-person sample size showed an acceptable accuracy level compared with standard devices when testing with 60 patient-samples with the mean errors heart rate of 1.22%, systolic blood pressure of 1.39%, diastolic blood pressure of 1.01%, and body temperature of 0.13%. According to testing results with 10 smart devices connected with the platform, the time delay caused by the distance between smart devices and the router is 10 s each round with the longest outdoor distance of 200 m. As there is a short-time delay, it does not affect the working ability of the smart system. It is still making the proposed system be able to show patient's status and function in emergency cases.