Remote Healthcare Applications Research Articles

Thermal-energy-efficient-secured-link reliable and delay-aware routing protocol (TESLDAR) for wireless body area networks

Wireless Body Area Networks (WBANs) have emerged as a transformative technology for health monitoring, leveraging sensor nodes placed on or within the human body. This paradigm offers real-time insights into physiological parameters, enabling remote healthcare applications and enhancing patient well-being. However, the WBANs bring challenges that demand innovative solutions to ensure their seamless integration into healthcare ecosystems. Secondly, trustworthiness becomes paramount in healthcare applications, where the sensitivity of health-related data necessitates secure and reliable communication. Lastly, congestion issues, arising from bandwidth constraints and concurrent data transmissions, affect the performance of WBANs, impacting data integrity, energy consumption, and quality of service. Therefore, this paper proposes a TESLDAR protocol that incorporates the proposed Jellyfish Updated Aquila Narrow ExploExpand (JUANE) method aims to address issues such as hotspot-induced thermal stress, trustworthiness concerns, and congestion challenges. The proposed algorithm incorporates Jellyfish Optimization (JFO), inspired by the adaptable movement of jellyfish, with characteristics inspired by the Aquila bird. The method's innovative integration of jellyfish and bird-inspired behaviors positions it as a promising solution for efficient route maintenance in WBANs addressing thermal, trust, and congestion challenges for enhanced reliability in healthcare monitoring applications.

E-DigitTool: A New-Fangled Framework for Disease Prediction and Diagnosis in Remote Healthcare Applications

E-DigitTool: A New-Fangled Framework for Disease Prediction and Diagnosis in Remote Healthcare Applications

Laser communications system with drones as relay medium for healthcare applications.

This article introduces a prototype laser communication system integrated with uncrewed aerial vehicles (UAVs), aimed at enhancing data connectivity in remote healthcare applications. Traditional radio frequency systems are limited by their range and reliability, particularly in challenging environments. By leveraging UAVs as relay points, the proposed system seeks to address these limitations, offering a novel solution for real-time, high-speed data transmission. The system has been empirically tested, showcasing its ability to maintain data transmission integrity under various conditions. Results indicate a substantial improvement in connectivity, with high data transmission success rate (DTSR) scores, even amidst environmental disturbances. This study underscores the system's potential for critical applications such as emergency response, public health monitoring, and extending services to remote or underserved areas.

Remote ECG Monitoring System via Internet

Electrocardiography (ECG) is a non-invasive diagnostic tool that records the electrical activity of the heart. It is a valuable tool for diagnosing a variety of heart conditions, including arrhythmias, heart failure, and myocardial infarction. This review paper explores the landscape of Real-time Electrocardiogram (ECG) Monitoring Systems via the Internet, analyzing advancements, challenges, and future prospects in this domain. The study investigates the integration of ECG monitoring systems with internet connectivity, enabling real- time data transmission and analysis for remote healthcare applications. Examining the utilization of IoT frameworks, particularly Raspberry Pi and Arduino, the review elucidates their role in establishing connectivity and data transmission protocols. It assesses the impact of IoT-enabled ECG systems on enhancing patient care, enabling telemedicine, and improving healthcare accessibility. Keywords— Internet of Things (IoT), Raspberry Pi R3, Arduino UNO, Remote Monitoring, Electrocardigraphy (ECG), Internet connected Medical Devices, Remote Healthcare

Current security and privacy posture in wireless body area networks

Wireless Body Area Networks (WBANs) have emerged as a promising technology for remote health monitoring and healthcare applications. However, ensuring the security and privacy of sensitive health data in WBANs is crucial to foster user trust and prevent unauthorized access or data breaches. This paper provides an overview of the key challenges, techniques, and research gaps in WBAN security and privacy. The findings indicate that the challenges in WBAN security and privacy include resource constraints, compatibility issues, privacy concerns, dynamic network environments, security and usability trade-offs, emerging threat landscape, and user awareness and education. To address these challenges, various security techniques have been developed, such as authentication and authorization mechanisms, encryption, access control, secure communication protocols, intrusion detection systems, and privacy-preserving data handling techniques. Despite the progress made, there are research gaps that require further investigation. These research gaps include the development of secure and lightweight authentication mechanisms, privacy-preserving data analysis techniques, trust and security management frameworks, resilience to insider threats, security of data aggregation and fusion, user-centric security designs, and addressing legal and ethical considerations. Addressing these research gaps and challenges requires collaboration between researchers, device manufacturers, policymakers, and end-users. Ongoing research and innovation are necessary to develop robust security techniques, privacy-enhancing technologies, and user-friendly solutions tailored for WBANs. Additionally, compliance with privacy regulations, user education, and awareness are critical for responsible and ethical use of WBANs.

Multiple-Dimension Biohealth Data Acquisition System for 5G Telehealthcare Application

A 5G-enabled digital multiple-dimension biohealth data acquisition system prototype integrating an electrochemical dongle, a photochemical dongle and a fluorescence sensor for remote and smart healthcare application is proposed in this paper. The system evolves from the Internet of Medical Things (IoMT) and can automatically collect multiple biochemical indicators including chronic disease markers, renal function indicators, and inflammatory factors from patients' blood samples with rapid speed and high accuracy. Because the system is based on Massive Machine Type Communications (mMTC), it can interact reliably with hospital systems or personal mobile devices with ultra-low latency. To generate a platform for in-time interaction between patients and medical staff, an Application (App) terminal is developed to guarantee real-time medical data sharing and face-to-face remote consultation. This not only promotes the personal proactive health management, but also generates valuable records to hospitals for future prevention of diseases, which is highly practical for tele-health management.

Wi-Breath: A WiFi-Based Contactless and Real-Time Respiration Monitoring Scheme for Remote Healthcare.

Respiration rate is an important healthcare indicator, and it has become a popular research topic in remote healthcare applications with Internet of Things. Existing respiration monitoring systems have limitations in terms of convenience, comfort, and privacy, etc. This paper presents a contactless and real-time respiration monitoring system, the so-called Wi-Breath, based on off-the-shelf WiFi devices. The system monitors respiration with both the amplitude and phase difference of the WiFi channel state information (CSI), which is sensitive to human body micro movement. The phase information of the CSI signal is considered and both the amplitude and phase difference are used. For better respiration detection accuracy, a signal selection method is proposed to select an appropriate signal from the amplitude and phase difference based on a support vector machine (SVM) algorithm. Experimental results demonstrate that the Wi-Breath achieves an accuracy of 91.2% for respiration detection, and has a 17.0% reduction in average error in comparison with state-of-the-art counterparts.

A lightweight blockchain and fog-enabled secure remote patient monitoring system

IoT has enabled the rapid growth of smart remote healthcare applications. These IoT-based remote healthcare applications deliver fast and preventive medical services to patients at risk or with chronic diseases. However, ensuring data security and patient privacy while exchanging sensitive medical data among medical IoT devices is still a significant concern in remote healthcare applications. Altered or corrupted medical data may cause wrong treatment and create grave health issues for patients. Moreover, current remote medical applications’ efficiency and response time need to be addressed and improved. Considering the need for secure and efficient patient care, this paper proposes a lightweight Blockchain-based and Fog-enabled remote patient monitoring system that provides a high level of security and efficient response time. Simulation results and security analysis show that the proposed lightweight blockchain architecture fits the resource-constrained IoT devices well and is secure against attacks. Moreover, the augmentation of Fog computing improved the responsiveness of the remote patient monitoring system by 40%.

A Survey of Federated Learning From Data Perspective in the Healthcare Domain: Challenges, Methods, and Future Directions

Recent advances in deep learning (DL) have shown that data-driven insights can be used in smart healthcare applications to improve the quality of life for patients. DL needs more data and diversity to build a more accurate system. To satisfy these requirements, more data need to be pooled at the centralized server to train the model deeply, but the process of pooling faces privacy and regulatory challenges. To settle them, the concept of sharing model learning rather than sharing data through federated learning (FL) is proposed. FL creates a more reliable system without transferring data to the server, resulting in the right system with stronger security and access rights to data that protect privacy. This research aims to (1) provide a literature review and an in-depth study on the roles of FL in the fields of healthcare; (2) highlight the effectiveness of current challenges facing standardized FL, including statistical data heterogeneity, privacy and security concerns, expensive communications, limited resources, and efficiency; and (3) present lists of open research challenges and recommendations for future FL for the academic and industrial sectors in telemedicine and remote healthcare applications. An extensive review of the literature on FL from a data-centric perspective was conducted. We searched the Science Direct, IEEE Xplore, and PubMed databases for publications published between January 2018 and February 2023. A new crossover matching between the approaches that solve or mitigate all types of skewed data has been proposed to open up opportunities to other researchers. In addition, a list of various applications was organized by learning application task types such as prediction, diagnosis, and classification. We think that this study can serve as a helpful manual for academics and industry professionals, giving them guidance and important directions for future studies.

An Effective Self-Configurable Ransomware Prevention Technique for IoMT.

Remote healthcare systems and applications are being enabled via the Internet of Medical Things (IoMT), which is an automated system that facilitates the critical and emergency healthcare services in urban areas, in addition to, bridges the isolated rural communities for various healthcare services. Researchers and developers are, to date, considering the majority of the technological aspects and critical issues around the IoMT, e.g., security vulnerabilities and other cybercrimes. One of such major challenges IoMT has to face is widespread ransomware attacks; a malicious malware that encrypts the patients' critical data, restricts access to IoMT devices or entirely disable IoMT devices, or uses several combinations to compromise the overall system functionality, mainly for ransom. These ransomware attacks would have several devastating consequences, such as loss of life-threatening data and system functionality, ceasing emergency and life-saving services, wastage of several vital resources etc. This paper presents a ransomware analysis and identification architecture with the objective to detect and validate the ransomware attacks and to evaluate its accuracy using a comprehensive verification process. We first develop a comprehensive experimental environment, to simulate a real-time IoMT network, for experimenting various types of ransomware attacks. Following, we construct a comprehensive set of ransomware attacks and analyze their effects over an IoMT network devices. Furthermore, we develop an effective detection filter for detecting various ransomware attacks (e.g., static and dynamic attacks) and evaluate the degree of damages caused to the IoMT network devices. In addition, we develop a defense system to block the ransomware attacks and notify the backend control system. To evaluate the effectiveness of the proposed framework, we experimented our architecture with 194 various samples of malware and 46 variants, with a duration of sixty minutes for each sample, and thoroughly examined the network traffic data for malicious behaviors. The evaluation results show more than 95% of accuracy of detecting various ransomware attacks.

Multiple-symbol noncoherent learning detection of coded QAM signals in IEEE 802.15.3 Wireless Multi-media Networks

We consider the noncoherent deep learning problem for coded signal detection under the phase noncoherent channels for remote home healthcare applications with high data rate. In particular, a multiple-symbol noncoherent learning detection (MNLD) scheme based on neural networks is proposed for low-density parity-check (LDPC) coded noncoherent quadrature amplitude modulation (QAM) signals in IEEE 802.15.3 Wireless Multi-media Networks. Our derivation shows that extensive operations for the first kind zero-order modified Bessel function is unavoidable for the implementation of the optimal bit log-likelihood ratio (LLR) for decoding in traditional multiple-symbol detection (MSD) scheme. The perfect estimation of the channel state information (CSI), i.e., a priori information about the variance of the additive white Gaussian noise (AWGN), is also required for the receiver. This is clearly not computationally practical for Wireless Multi-media Networks. Consequently, we developed an improved approach based on feed-forward neural networks to accurately calculate the bit LLR. Furthermore, to decrease the generation size of training set and thus increase the training speed of the proposed neural networks, we uniformly quantize the continuous carrier phase offset (CPO), which is random and unknown, into discrete status. Our simulation results verify the learning efficiency of this simplified training-set generation configuration. The decoding convergence is successfully accelerated and much performance gain is finally achieved when compared with traditional decoding using the perfect bit LLR. This is clearly critical for high reliable transmission of home healthcare information.

(Invited, Digital Presentation) Heterogeneously Integrated Nanomaterial-Based Multimodal Flexible Sensor Sheets

Human interaction is of great interests for a next class of human society to realize convenient, comfortable, and safe life. For this purpose, multimodal sensing is one of the most important functionalities. Furthermore, without sacrificing the original shape change or uncomfortability to attach or wear the sensor systems onto the targeted surface, respectively, ultrathin and mechanically flexible sensor is required. To address these requirements together, one potential material system is inorganic nanomaterials, which allows it to achieve a variety of material integrated formation on a film and mechanical flexibility due to ultra-small dimensions with high performance. However, the challenges to utilize the nanomaterials for the practical flexible device applications are still remained such as scalability, long-time stability, and multi-functional integrations. In my group, we are trying to develop the heterogeneously integrated nanomaterial-based sensors on flexible films to overcome all challenges to move forward to building the new sensor platforms.In this talk, as the progress on our research activity to contribute the flexible sensor community and IoT applications, multimodal flexible sensors using mainly nanomaterials formed by solution-based and laser-induced methods are discussed for wearable healthcare/medical applications. In addition to the flexible sensors, wireless system and signal processing for the feedback alarm function when the abnormal signal is detected are introduced as the potential flexible sensor application. In particular, electrocardiogram (ECG), skin temperature, and skin humidity flexible sensors and their integration on a flexible film are explained by using fundamental characteristics.This study focuses on the heterogeneously integrated multimodal flexible sensors for the application of remote healthcare applications. Although additional functional sensors and systems to realize more useful and convenient home-use healthcare system is required, the approaches may contribute the further developments to open a next class of electronics for IoT.

Heart Failure Prediction Technique using Complex Event Processing

According to the WHO (World Health Organization) chronic diseases such as cancer, coronary heart disease, diabetes mellitus type 2, and chronic obstructive pulmonary diseases are among the world's most common diseases constitute because of this about 60% of all deaths occur in world. Here, we propose new health monitoring techniques to the prediction of heart failures. In this, we propose edge-computing based Complex Event Processing (CEP) techniques with the Remote Patient Monitoring (RPM) for the remote healthcare applications. This approach is based on the CEP it is combined with the statistical approach. For the extraction heart defects of patients C4.5 algorithm and, to the prediction of heart failure multilayer perceptron (MLP) model will be consider. First phase is to collects health parameters. Second phase is to process the collected data using an analysis rule. This proposed system continuously monitors heart patient and it predicts heart failures strokes based on the related symptoms. When a critical condition occurs then it alters patients and cardiologist.

An energy-efficient and secure framework for IoMT: An application of smart cities

The Internet of Medical Things (IoMT) has played an unrivaled role in rendering the ever-growing advancements in remote healthcare and sensor informatics. The body-sensor devices employed in remote healthcare applications are not only resource-constrained but are also vulnerable to various security attacks. Hence, to pact with the energy limitations and security vulnerability of these body-sensor devices, in this paper, we suggest ESRO: An Energy-efficient and Secured Communication framework using recently proposed meta-heuristic approach, i.e., Remora Optimization Algorithm (ROA). We employ a distributed cluster-based routing mechanism with the novel secured selection of Cluster Head (CH) using ROA. We ensure secured transmission using risky mode and γ risky mode. Extensive simulation of the ESRO demonstrates the supremacy of the proposed work over the existing techniques when evaluated on a benchmark of different performance metrics. ESRO not only preserves the energy but also ensures secured communication for IoMT.

Artifacts Reduction Using Multi-Scale Feature Attention Network in Compressed Medical Images

Medical image compression is one of the essential technologies to facilitate real-time medical data transmission in remote healthcare applications. In general, image compression can introduce undesired coding artifacts, such as blocking artifacts and ringing effects. In this paper, we proposed a Multi-Scale Feature Attention Network (MSFAN) with two essential parts, which are multi-scale feature extraction layers and feature attention layers to efficiently remove coding artifacts of compressed medical images. Multi-scale feature extraction layers have four Feature Extraction (FE) blocks. Each FE block consists of five convolution layers and one CA block for weighted skip connection. In order to optimize the proposed network architectures, a variety of verification tests were conducted using validation dataset. We used Computer Vision Center-Clinic Database (CVC-ClinicDB) consisting of 612 colonoscopy medical images to evaluate the enhancement of image restoration. The proposed MSFAN can achieve improved PSNR gains as high as 0.25 and 0.24 dB on average compared to DnCNN and DCSC, respectively.

SECURE MEDICAL IMAGE ENCRYPTION FOR REMOTE VIRTUAL DOCTOR SYSTEM BASED ON H-IOT APPLICATIONS OVER 5G NETWORK: A COMPARISON STUDY

Abstract. Recent years have seen a new amalgamation between Remote virtual doctor systems and healthcare IoT applications which plays a crucial role in enhancing patient’s healthcare life. The Remote VDS connected with smart Healthcare devices through the wireless network to be accessible anytime and anywhere is anticipated to treat rapidly valuable and confidential data such as personal medical images. Therefore, quick medical image encryption is an essential task in healthcare topic. Some medical image encryption research like the Scan methodology proposed for Remote VDS suffers from a long computational time. Then, the patient will confront difficulty on treatment availability in real-time and especially for critical situations. To overcome this problem, in this work, three medical image encryption methods are compared, (1) A Lightweight Encryption Technique to Enhance Medical Image Security on Internet of Medical Things Applications, (2) A new chaotic map with dynamic analysis and encryption application on the Internet of Health Things, and (3) Robust medical image encryption based on DNA chaos method. The comparison results have proven that the new chaotic map with a dynamic analysis scheme has high-security analysis and performances with low computational time compared to other methods. That makes it a good candidate for the H-IOT-RVDS environment connecting over 5G technology like a cellular network to enable connectivity between nodes.

PO-1976 Has the Covid-19 Pandemic increased willingness to engage with remote collection of outcome data?

Purpose or Objective: Remote healthcare applications have been developing over many years, however clinical implementation has been cautious. As society has embraced technology during the Covid-19 pandemic, the question is: have attitudes towards remote healthcare technology also changed? One particular aspect of healthcare that could be addressed remotely is the collection of patient reported outcome measures (PROMs) using a digital voice assistant (DVA) such as the Amazon Echo (Alexa). This could improve both the frequency and quality of data collected. Materials and Methods: A questionnaire was developed to evaluate patient and carer opinions on using a DVA to report radiotherapy-related side effects. This was distributed to the adult patients in the radiotherapy department in February 2019 and to the Teenager, Young Adult and Paediatric radiotherapy department in mid 2019. It was redistributed in September 2020, allowing a pre/post Covid-19 comparison. To maximise engagement with the questionnaire, it was decided to ask a small number of simple questions. These four questions sought to ascertain whether the respondent was a patient or carer, respondent age (decade grouping), how knowledgeable they were about the Amazon Echo, and how willing they would be to participate in a hypothetical study in which they would use an Amazon Echo to record the side effects they experienced as a result of their cancer treatment. A comments box was included to allow participants to convey additional information. Results: 85 completed questionnaires were collected in 2019 and 43 in 2020. The median age of the initial survey cohort was 60-69 years old whilst for the second cohort was 50-59 years old. The slight difference in median age of the cohorts is likely a reflection on modifications to clinical practice due to Covid-19. 16% of the initial cohort and 19% of the second cohort were carers. 67% of the initial cohort responded that they had some or good knowledge of Amazon Echo, this increased to 84% in the second cohort (fig. 1). Willingness to participate was highest in the group of patients with good knowledge. 44% of the initial cohort were willing to take part in a study using an Amazon Echo to record side effects, increasing to 58% in the subsequent cohort (fig. 2). However, the percentage of respondents who were not willing to take part also increased from 32% to 37% with a reduction in the number of respondents who didn’t know (25% to 5%). Willingness to participate varied with age. Comments were diverse and included concerns about using technology and data security but also positive comments on the potential to reduce hospital visits. (Figure Presented) Conclusion: Overall, the survey results indicate an increased knowledge of DVA. There was also an increased willingness to engage with a DVA for remote collection of radiotherapy outcome data following the first wave of the Covid-19 pandemic.

Minimizing path loss and improving security in wireless body area networks

PurposeThe purpose of this paper is energy consumption and security. To extend the sensor’s life span, saving the energy in a sensor is important. In this paper, biosensors are implanted or suited on the human body, and then, transposition has been applied for biosensors for reducing the sensor distance from the sink node. After transposition path loss has been calculated, security is maintained and also compared the results with the existing strategies.Design/methodology/approachNowadays, one of the most emergent technologies is wireless body area network (WBAN), which represents to improve the quality of life and also allow for monitoring the remote patient and other health-care applications. Traffic routing plays a main role together with the relay nodes, which is used to collect the biosensor’s information and send it towards the sink.FindingsTo calculate the distance and observe the position, Euclidean distance technique is used. Path loss is the main parameter, which is needed to reduce for making better data transmission and to make the network stability. Routing protocols can be designed, with the help of proposed values of sensors locations in the human body, which gives good stability of network and lifetime. It helps to achieve as the less deplete energy.Originality/valueThis scheme is compared with the two existing schemes and shows the result in terms of parameter path loss. Moreover, this paper evaluated a new method for improving the security in WBAN. The main goal of this research is to find the optimal sensor location on the body and select the biosensor positions where they can get less energy while transmitting the data to the sink node, increasing the life span in biosensors, decreasing memory space, giving security, controlling the packet complexity and buffer overflow and also fixing the damages in the existing system.

Energy Efficiency Solutions for IEEE 802.15.6 Based Wireless Body Sensor Networks

IEEE 802.15.6 standard has been designed for wireless body sensor networks (WBSNs) that consist of several sensors and a coordinator node in, on or around the human body. In WBSNs, the body sensors continuously send their data to the coordinator node for remote healthcare applications. Continuously sensing body signals is a requirement for vital signs but continuously sending these signals to a destination over coordinator node is not necessary. Measured signs may be in a normal range for a healthy person, so these measurements may not be transmitted to a destination. In this study, the event-driven approach in an IEEE 802.15.6 based WBSN architecture are examined. If a vital sign exceeds the normal range in the proposed architecture, the corresponding sensor must send the sign to the coordinator node. In addition, a WBSN architecture is designed with the energy harvesting capabilities for purposing energy efficiency in a different way. Comparative performance analysis of three WBSN; traditional WBSN, event-driven WBSN, and energy harvesting aware WBSN is given in this study to show the impacts of energy efficiency methods to WBSNs. The event-driven scheme outperforms traditional WBSN, with a delay of 21% and energy consumption of 67% and the proposed energy harvesting aware scheme provides 5% additional energy to the traditional WBSN. Simulation results show that our proposed methods yield much better performance than the traditional approach.

A Video-Based Technique for Heart Rate and Eye Blinks Rate Estimation: A Potential Solution for Telemonitoring and Remote Healthcare.

Current telemedicine and remote healthcare applications foresee different interactions between the doctor and the patient relying on the use of commercial and medical wearable sensors and internet-based video conferencing platforms. Nevertheless, the existing applications necessarily require a contact between the patient and sensors for an objective evaluation of the patient’s state. The proposed study explored an innovative video-based solution for monitoring neurophysiological parameters of potential patients and assessing their mental state. In particular, we investigated the possibility to estimate the heart rate (HR) and eye blinks rate (EBR) of participants while performing laboratory tasks by mean of facial—video analysis. The objectives of the study were focused on: (i) assessing the effectiveness of the proposed technique in estimating the HR and EBR by comparing them with laboratory sensor-based measures and (ii) assessing the capability of the video—based technique in discriminating between the participant’s resting state (Nominal condition) and their active state (Non-nominal condition). The results demonstrated that the HR and EBR estimated through the facial—video technique or the laboratory equipment did not statistically differ (p > 0.1), and that these neurophysiological parameters allowed to discriminate between the Nominal and Non-nominal states (p < 0.02).