Technology For Healthcare Applications Research Articles

Federated Learning for Clinical Event Classification Using Vital Signs Data

Accurate and timely diagnosis is a pillar of effective healthcare. However, the challenge lies in gathering extensive training data while maintaining patient privacy. This study introduces a novel approach using federated learning (FL) and a cross-device multimodal model for clinical event classification based on vital signs data. Our architecture employs FL to train several machine learning models including random forest, AdaBoost, and SGD ensemble models on vital signs data. The data were sourced from a diverse clientele at a Boston hospital (MIMIC-IV dataset). The FL structure trains directly on each client’s device, ensuring no transfer of sensitive data and preserving patient privacy. The study demonstrates that FL offers a powerful tool for privacy-preserving clinical event classification, with our approach achieving an impressive accuracy of 98.9%. These findings highlight the significant potential of FL and cross-device ensemble technology in healthcare applications, especially in the context of handling large volumes of sensitive patient data.

Advances and Challenges in IoT-Based Smart Drug Delivery Systems: A Comprehensive Review

In the current era of technology, the internet of things (IoT) plays a vital role in smart drug delivery systems. It is an emerging field that offers promising solutions for improving the efficacy, safety, and patient compliance of drug therapies. IoT-based drug delivery systems leverage advanced devices, sophisticated sensors, and smart tools to monitor and analyse the health matrices of the patient in real-time, allowing for personalised and targeted drug delivery. This technology is implemented through various types of devices, including wearable and implantable devices such as infusion pumps, smart pens, inhalers, and auto-injectors. However, the development and implementation of IoT-based drug delivery systems pose several challenges, such as ensuring data security and privacy, regulatory compliance, compatibility, and reliability. In this paper, the latest research on smart wearable devices and its analysis are addressed. It also focuses on the challenges of ensuring the safe and efficient use of this technology in healthcare applications.

Detection of fetal brain abnormalities using data augmentation and convolutional neural network in internet of things

The all-embracing morphological changes in fetal brain development in the whole time pregnancy are visually seen in acquisition of Magnetic Resonance Imaging (MRI) techniques. In the way of exploring fetal brain development, Convolutional Neural Network (CNN) technique brings out automatic segmentation and classification. Monitoring fetal brain development is crucial to prevent brain abnormality. With the automation and resource optimization provided by IOT technology in healthcare applications, the national healthcare industries can improve the quality of care while lowering costs. The remarkable progresses in image recognition tasks are undergone via CNN to recognize complex patterns in image data. It faces the standstill challenges in observing the fetal brain development inclusive of 16–39 weeks gestation images and explores the optimized performance evaluation for quantitative assessment. The goal of this research is to improve access to brain development information by utilizing modern medical detection method as well as embedding IoT (internet of things). The brain malformations are considered as a problem in recent days, to overcome this problem the Detection of Fetal Brain Abnormalities (DFBA) using Data Augmentation is proposed in this paper and the IOT technology is helps to identify the detection in easy way. The proposed DFBA perform in three stages image pre-processing, model build and performance evaluation. The input dataset comprises 875 MRI scan images taken from the Harvard medical school with the training set as 80%, testing set as 10%, and validation set as 10%. Once the data is retrieved, the preprocessing techniques are carried out using Normalization and Reshaping for better progress and result. In sequence to pre-processing, Data augmentation is applied to increase the size of the dataset for CNN to be processed for classification. The efficiency of the proposed DFBA approach has been determined using the evaluation metrics such as Recall, F1-score, Accuracy, Precision, Confusion matrix and Support. The proposed method peculiarly tests set accuracy as 83% are obtained which is better than the existing classifiers.

A Review of Blockchain Technology Based Techniques to Preserve Privacy and to Secure for Electronic Health Records

Research has been done to broaden the block chain’s use cases outside of finance since Bitcoin introduced it. One sector where block chain is anticipated to have a big influence is healthcare. Researchers and practitioners in health informatics constantly struggle to keep up with the advancement of this field's new but quickly expanding body of research. This paper provides a thorough analysis of recent studies looking into the application of block chain based technology within the healthcare sector. Electronic health records (EHRs) are becoming a crucial tool for health care practitioners in achieving these objectives and providing high-quality treatment. Technology and regulatory barriers, such as concerns about results and privacy issues, make it difficult to use these technologies. Despite the fact that a variety of efforts have been introduced to focus on the specific privacy and security needs of future applications with functional parameters, there is still a need for research into the application, security and privacy complexities, and requirements of block chain based healthcare applications, as well as possible security threats and countermeasures. The primary objective of this article is to determine how to safeguard electronic health records (EHRs) using block chain technology in healthcare applications. It discusses contemporary HyperLedgerfabrics techniques, Interplanar file storage systems with block chain capabilities, privacy preservation techniques for EHRs, and recommender systems.

Integration of Augmented Reality and Brain-Computer Interface Technologies for Health Care Applications: Exploratory and Prototyping Study.

BackgroundAugmented reality (AR) and brain-computer interface (BCI) are promising technologies that have a tremendous potential to revolutionize health care. While there has been a growing interest in these technologies for medical applications in the recent years, the combined use of AR and BCI remains a fairly unexplored area that offers significant opportunities for improving health care professional education and clinical practice. This paper describes a recent study to explore the integration of AR and BCI technologies for health care applications.ObjectiveThe described effort aims to advance an understanding of how AR and BCI technologies can effectively work together to transform modern health care practice by providing new mechanisms to improve patient and provider learning, communication, and shared decision-making.MethodsThe study methods included an environmental scan of AR and BCI technologies currently used in health care, a use case analysis for a combined AR-BCI capability, and development of an integrated AR-BCI prototype solution for health care applications.ResultsThe study resulted in a novel interface technology solution that enables interoperability between consumer-grade wearable AR and BCI devices and provides the users with an ability to control digital objects in augmented reality using neural commands. The article discusses this novel solution within the context of practical digital health use cases developed during the course of the study where the combined AR and BCI technologies are anticipated to produce the most impact.ConclusionsAs one of the pioneering efforts in the area of AR and BCI integration, the study presents a practical implementation pathway for AR-BCI integration and provides directions for future research and innovation in this area.

Perceptions of Indonesian Nurses Toward the Application of the Internet of Things in the Future

In the healthcare industry, Internet of Things (IoT) based systems are utilized for phone care delivery, smart healthcare, smart health sensors and wearable technology, preventative systems, and distant monitoring. No studies have assessed nurses’ perspectives of IoT applications in Indonesia. The purpose of this study was to identify health professionals’ perspectives on future health and technology trends, to identify their readiness to adopt new health technologies, and to identify the use of IoT technology in healthcare applications. This study was conducted using a descriptive and cross-sectional approach. Nurses with a Diploma III and at least one year of experience were selected from three different departments (medical surgical, pediatrics, maternity). About 76% of 350 nurses had little knowledge of IoT technology and 95% said they do not keep up to date with IoT publications. About 50% of nurses believed that IoT technology will have a large impact on the health and education sectors. These results indicated that IoT technology and informatics should be included in nursing education and further studies should be conducted to integrate technological trends into healthcare and nursing practices.
 Keywords: perceptions, nurses, internet of things, healthcare

Potentially Wearable Thermo‐Electrochemical Cells for Body Heat Harvesting: From Mechanism, Materials, Strategies to Applications

AbstractWearable electronics are becoming one of the key technologies in health care applications including health monitoring, data acquisitions, and real‐time diagnosis. The commercialization of next‐generation devices has been stymied by the lack of ultrathin, flexible, and reliable power sources. Wearable thermo‐electrochemical cells (TECs), which can convert body heat to electricity via an electrochemical process, are showing great promise as power sources for such wearable systems. TECs harvest orders of magnitude more voltage per temperature difference (Seebeck coefficient (1–34 mV K−1)) when compared to the more common thermoelectric generators (Seebeck coefficient ≈tens or hundreds of µV K−1). However, there still remain great challenges for TECs progressing towards wearable applications. This review summarizes the recent development of potentially wearable TECs with promise for body‐heat harvesting, with a specific focus on flexible electrode materials, solid‐state electrolytes, device fabrication, and strategies toward applications. It also clarifies the challenges and gives some future direction to enhance future investigations on high‐performance wearable TECs for practical and self‐powered wearable devices.

Internet of Things (IoT): A Review of Its Enabling Technologies in Healthcare Applications, Standards Protocols, Security, and Market Opportunities

The Internet of Things (IoT) is a methodology or a system that encompasses real-world things to interact and communicate with each other with the assistance of networking technologies. This article describes surveys on advances in IoT-based healthcare methods and reviews the state-of-the-art technologies in detail. Moreover, this review classifies an existing IoT-based healthcare network and represents a summary of all perspective networks. IoT healthcare protocols are analyzed in this context and provide a broad discussion on it. It also initiates a comprehensive survey on IoT healthcare applications and services. Extensive insights into IoT healthcare security, its requirements, challenges, and privacy issues are visualized in IoT surrounding healthcare. In this review, we analyze security and privacy features consisting of data protection, network architecture, Quality of Services (QoS), app development, and continuous monitoring of healthcare that are facing difficulties in many IoT-based healthcare architectures. To mitigate the security problems, an IoT-based security architectural model has been proposed in this review. Furthermore, this review discloses the market opportunity that will enhance the IoT healthcare market development. To conduct the survey, we searched through established journal and conference databases using specific keywords to find scholarly works. We applied a filtering mechanism to collect only papers that were relevant to our research works. The selected papers were then examined carefully to understand their contributions/research focus. Eventually, the paper reviews were analyzed to identify any existing research gaps and untouched areas of research and to discover possible features for sustainable IoT healthcare development.

Real-time Health Monitoring System Using Wireless Body Area Network

Wireless body area network (WBAN) being a sub-domain of wireless sensor network (WSN) is a new emerging technology for healthcare applications. A WBAN consists of low-power tiny wireless nodes placed on or around the human body that continuously observe vital health signs of a patient. These sensors are capable of sending information of physiological parameters taken from human body to other devices for diagnosis procedures and prescription. WBAN provides ubiquitous healthcare services and enables greater mobility without restricting human normal activities, as the medical personnel can observe the patient health conditions based on the data received through the wireless network. This research work provides a WBAN based healthcare monitoring system that can provide the electrocardiogram (ECG), heartbeat, and human body temperature information. The wireless transmission of the received data from human body is performed by using Zigbee IEEE802.15.4 communication standard. The physiological data will be communicated to remote medical server where data is stored and analyzed. In case any disease is diagnosed, medical personnel can provide immediate assistance to the patients.

Internet of Things ‐ integrated IR‐UWB technology for healthcare applications

SummaryRecent technology developments have produced small and smart biomedical sensors, which can be worn or implanted in the human body. These biosensors create closed wireless networks named Wireless Body Area Networks (WBAN). The WBAN will continuously observe the physiological state of patients for both diagnosis and prevention. Those include on‐body measurements such as the Electrocardiogram (ECG), Electroencephalogram (EEG), temperature, and blood pressure. Ultra‐Wide‐Band (UWB) is a technology that has received a lot of attention due to several unique features such as secure transmission, low noise, and low energy consumption. Given the fact that the patients' well‐being might be dependent on the accurate realization of such networks, a high level of design and implementation accuracy are maintained throughout the system. In this paper, we proposed an Impulse‐Radio Ultra‐Wideband system, which is composed of static biomedical nodes mounted on a patient's body to collect vital data and send it wirelessly to a central node or subsequent analysis by healthcare professionals. The performance of this network, such as the effect of node location, the number of transmitted symbols, multiuser interference, and intersymbol interference, is evaluated. We also study the physical layer and quality of service of this proposed architecture.

IBEET-RSA: Identity-Based Encryption with Equality Test over RSA for Wireless Body Area Networks

Wireless body area network (WBAN) constitutes a widely implemented technique for remote acquisition and monitoring of patient health-related information via the use of embodied sensors. Given that security and privacy protection, including, but not limited to user authentication, integrity, and confidentiality, are both key challenges and a matter of deep concern when it comes to the deployment of emerging technologies in healthcare applications, state-of-the-art measures and solutions are needed to fully address security and privacy concerns in an effective and sensible manner by considering all the benefits and limitation of remote healthcare systems. In this paper, we proposed an efficient and secure identity-based encryption scheme under the RSA assumption providing equality test. We then proved the security of our scheme for one-way secure against chosen-identity and chosen-ciphertext attacks (OW-ID-CCA) by means of the random oracle model. The performance evaluation results indicated that our scheme outperforms other security schemes in terms of providing relatively low computational cost and stable compatibility with WBAN applications.

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Smart Body Monitoring system using IoT and Machine Learning

In the modern health care environment, the usage of IoT technologies brings convenience of physicians and patients, since they are applied to various medical areas. The body sensor network (BSN) technology is one of the core technologies of IoT developments in healthcare system, where a patient can be monitored using a collection of tiny-powered and lightweight wireless sensor nodes. However, the development of this new technology in healthcare applications without considering security makes patient privacy vulnerable. In this paper, at first, we highlight the major security requirements in BSN-based modern healthcare system. Subsequently, we propose a secure IoT-based healthcare system using BSN, called BSN-Care, which can efficiently accomplish those requirements. The body sensor network (BSN) technology is one of the most imperative technologies used in IoT-based modern healthcare system. It is basically a collection of low-power and lightweight wireless sensor nodes that are used to monitor the human body functions and surrounding environment. Since BSN nodes are used to collect sensitive (life-critical) information and may operate in hostile environments, accordingly, they require strict security mechanisms to prevent malicious interaction with the system.

Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach.

Recent technological advancement in wireless communication has led to the invention of wireless body area networks (WBANs), a cutting-edge technology in healthcare applications. WBANs interconnect with intelligent and miniaturized biomedical sensor nodes placed on human body to an unattended monitoring of physiological parameters of the patient. These sensors are equipped with limited resources in terms of computation, storage, and battery power. The data communication in WBANs is a resource hungry process, especially in terms of energy. One of the most significant challenges in this network is to design energy efficient next-hop node selection framework. Therefore, this paper presents a green communication framework focusing on an energy aware link efficient routing approach for WBANs (ELR-W). Firstly, a link efficiency-oriented network model is presented considering beaconing information and network initialization process. Secondly, a path cost calculation model is derived focusing on energy aware link efficiency. A complete operational framework ELR-W is developed considering energy aware next-hop link selection by utilizing the network and path cost model. The comparative performance evaluation attests the energy-oriented benefit of the proposed framework as compared to the state-of-the-art techniques. It reveals a significant enhancement in body area networking in terms of various energy-oriented metrics under medical environments.

A Novel Implementation for Patient Monitoring in Rural Areas Using IOT Technology

Now a day’s there is a rapid growth in technology. In the modern health care environment, the usage of IOT technologies brings convenience of physicians and patients since they are applied to various medical areas. The development of this new technology in healthcare applications without considering security makes patient privacy vulnerable. In this paper at first we highlight the major security requirements while storing the patient information in the cloud. Through IOT Doctor’s Treatment suggestions, the patient updated treatment information will be directly stored in the cloud. In order to achieve effective for storing the information in cloud we provide secure communication protocols. We also propose a secure IOT based on sensors used in modern healthcare.

BSN-Care: A Secure IoT-Based Modern Healthcare System Using Body Sensor Network

Advances in information and communication technologies have led to the emergence of Internet of Things (IoT). In the modern health care environment, the usage of IoT technologies brings convenience of physicians and patients, since they are applied to various medical areas (such as real-time monitoring, patient information management, and healthcare management). The body sensor network (BSN) technology is one of the core technologies of IoT developments in healthcare system, where a patient can be monitored using a collection of tiny-powered and lightweight wireless sensor nodes. However, the development of this new technology in healthcare applications without considering security makes patient privacy vulnerable. In this paper, at first, we highlight the major security requirements in BSN-based modern healthcare system. Subsequently, we propose a secure IoT-based healthcare system using BSN, called BSN-Care, which can efficiently accomplish those requirements.

IDetect: an intelligent intrusion detection system for wireless body area networks

Driven by recent technological advances in wireless communications, wireless sensors, and low power networked systems, wireless sensor networks (WSNs) are emerging as a promising technology in healthcare applications. Since information transmitted in these wireless body area networks (WBAN) often consists of critical and sensitive patient health and personal information, securing these networks is of central importance to their practical deployment in healthcare applications. The objective of this research is to design and develop intelligent intrusion detection techniques to improve security in WBAN. In this work, we propose iDetect, a multi-objective genetic algorithm based intrusion detection system (IDS) to provide optimal attack detection in these networks. The proposed algorithm guarantees that only those features necessary for detecting a specific attack is used in the intrusion detection process, thereby decreasing the computational complexity.

Guest Editorial

Over the past few years a significant growth of research involving the utilization of RF/microwave technologies in healthcare applications has been taking place. This "Special Issue on Biomedical Applications of RF/Microwave Technologies" is proof. Encouraged by funding from government agencies and private sources, and recognizing the emerging opportunities offered by high-frequency electronics and novel sensing technologies for advancing healthcare, researchers from various engineering disciplines have directed their interests to applications involving biological science and clinical medicine. The increase in RF/microwave-related activities targeting medical or biological problems is noticeable within the IEEE Microwave Theory and Techniques Society (IEEE MTT-S) community and beyond. These activities are broad in their scopes and involve multiple disciplines. They range from therapeutic, diagnostic, remote monitoring, and imaging applications of microwave technologies in clinical settings, as well as those involving sensing and communication over or through body tissues, where physiological or biochemical information are transmitted wirelessly, to the biological effects of these applications, in which the interaction of microwaves with tissues and living systems should be understood and manipulated. In addition, RF/microwave now serves as one of the key enabling technologies in innovative healthcare delivery and telemedicine. Three papers are on exposure systems for bioelectromagnetic research. Nine papers present radar-based systems for vital sign monitoring and object imaging. The remaining two radar papers focus on cancer detection by imaging. The Special Issue features papers from the US, UK, Japan, Korea, Australia, Canada, Saudi Arabia, Italy, Malaysia, Belgium, Germany, China, and France, indicating global efforts utilizing RF/microwave innovation for healthcare.

On the Security of A Dynamic ID-based Authentication Scheme for Telecare Medical Information Systems

Telecare medical information systems (TMISs) are increasingly popular technologies for healthcare applications. Using TMISs, physicians and caregivers can monitor the vital signs of patients remotely. Since the database of TMISs stores patients' electronic medical records (EMRs), only authorized users should be granted the access to this information for the privacy concern. To keep the user anonymity, recently, Chen et al. proposed a dynamic ID-based authentication scheme for telecare medical information system. They claimed that their scheme is more secure and robust for use in a TMIS. However, we will demonstrate that their scheme fails to satisfy the user anonymity due to the dictionary attacks. It is also possible to derive a user password in case of smart card loss attacks. Additionally, an improved scheme eliminating these weaknesses is also presented.

Security in Body Sensor Networks for Healthcare applications

This paper offers a depth review of numerous Wireless Sensor/detector Systems. Healthcare applications are considered as talented fields for Wireless Sensor Networks, where patients can be watched using wireless medical sensor networks (WMSNs). Present WMSN healthcare research trends center on patient mobility, patient reliable communication and energy-efficient routing. But, installing new technologies in healthcare applications without considering security and safety makes patient privacy in danger. Furthermore, the physiological data of an individual are extremely sensitive. So, security is a supreme requirement of healthcare systems, particularly in the case of patient privacy. This paper discusses the privacy and security issues in healthcare application using WMSNs

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