IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review

ACGAN for Addressing the Security Challenges in IoT-Based Healthcare System

The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people's lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets-biometric and network flow metric-have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results.

Nonfungible Tokens in Cardiovascular Medicine

The integration of nonfungible tokens (NFTs) in health care, particularly in cardiovascular medicine, represents a disruptive shift toward enhancing the security and interconnection of electronic health data around the patient. NFTs, unique digital certificates stored on a blockchain network, bind various sources of health data to their owner (the patient) and delineate the access rights for stakeholders (providers, researchers) using smart contracts. Data sources might include electronic medical records from different hospitals, clinics, pharmacies, test centres, and mHealth devices. Accordingly, patients and their providers benefit from seamless visibility of diagnoses, medications, electrocardiograms, imaging, home blood pressure logs, and artificial intelligence-enabled insights from these aggregated data. Rather than being stored on proprietary servers, data are encrypted and stored on decentralized networks with a unified point of access and immutable proof of ownership, making them more robust to theft or tampering. As custodians of their NFTs, patients are incentivized to actively partake in their health monitoring and self-driven research that aligns with their needs using innovative marketplaces that allow them to browse studies, document their informed consent, and monetize their contributions. Furthermore, they are empowered to educate themselves and seek care across siloes in traditional settings or virtual platforms such as the metaverse, where NFTs serve as digital passports. Despite these exciting prospects, adoption within the health care sector remains in its infancy, with ethical and technical limitations still being addressed. In this article we explore the multifaceted applications and key players in the field, and outline use-cases for patient-centred cardiovascular care featuring NFTs.

Application of noncontact sensors for cardiopulmonary physiology and body weight monitoring at home: A narrative review

Monitoring health status at home has garnered increasing interest. Therefore, this study investigated the potential feasibility of using noncontact sensors in actual home settings. We searched PubMed for relevant studies published until February 19, 2024, using the keywords "home-based," "home," "monitoring," "sensor," and "noncontact." The studies included in this review involved the installation of noncontact sensors in actual home settings and the evaluation of their performance for health status monitoring. Among the 3 included studies, 2 monitored respiratory status during sleep and 1 monitored body weight and cardiopulmonary physiology. Measurements such as heart rate, respiratory rate, and body weight obtained with noncontact sensors were compared with the results obtained from polysomnography, polygraphy, and commercial scales. All included studies demonstrated that noncontact sensors produced results comparable to those of standard measurement tools, confirming their excellent capability for biometric measurements. Overall, noncontact sensors have sufficient potential for monitoring health status at home.

From admission to discharge: a systematic review of clinical natural language processing along the patient journey

BACKGROUND

Medical text, as part of an electronic health record, is an essential information source in healthcare. Although natural language processing (NLP) techniques for medical text are developing fast, successful transfer into clinical practice has been rare. Especially the hospital domain offers great potential while facing several challenges including many documents per patient, multiple departments and complex interrelated processes.

METHODS

In this work, we survey relevant literature to identify and classify approaches which exploit NLP in the clinical context. Our contribution involves a systematic mapping of related research onto a prototypical patient journey in the hospital, along which medical documents are created, processed and consumed by hospital staff and patients themselves. Specifically, we reviewed which dataset types, dataset languages, model architectures and tasks are researched in current clinical NLP research. Additionally, we extract and analyze major obstacles during development and implementation. We discuss options to address them and argue for a focus on bias mitigation and model explainability.

RESULTS

While a patient's hospital journey produces a significant amount of structured and unstructured documents, certain steps and documents receive more research attention than others. Diagnosis, Admission and Discharge are clinical patient steps that are researched often across the surveyed paper. In contrast, our findings reveal significant under-researched areas such as Treatment, Billing, After Care, and Smart Home. Leveraging NLP in these stages can greatly enhance clinical decision-making and patient outcomes. Additionally, clinical NLP models are mostly based on radiology reports, discharge letters and admission notes, even though we have shown that many other documents are produced throughout the patient journey. There is a significant opportunity in analyzing a wider range of medical documents produced throughout the patient journey to improve the applicability and impact of NLP in healthcare.

CONCLUSIONS

Our findings suggest that there is a significant opportunity to leverage NLP approaches to advance clinical decision-making systems, as there remains a considerable understudied potential for the analysis of patient journey data.

Clinical Efficacy of Transcatheter Arterial Chemoembolization Combined With Percutaneous Microwave Coagulation Therapy for Advanced Hepatocellular Carcinoma

Background

The aim of the study was to explore the clinical efficacy of transcatheter arterial chemoembolization (TACE) combined with percutaneous microwave coagulation therapy (PMCT) for advanced hepatocellular carcinoma (HCC).

Methods

Eighty-three advanced HCC patients were divided into the experimental group (TACE + PMCT, 57 cases) and the control group (TACE alone, 26 cases). They received TACE treatment first, and computed tomography (CT) or hepatic artery angiography was performed 3 - 4 weeks after each treatment. Based on the comprehensive evaluation of iodine oil deficiency, fistula recanalization, residual lesions, and lesion progression, TACE or PMCT treatment was selectively performed, and three consecutive treatments were considered as one treatment cycle.

Results

The experimental group had a response rate (RR) of 49.1%, and the control group had a RR of 38.4%. The reduction rate of alpha-fetoprotein (AFP) in the experimental group was significantly higher than the control group (P < 0.05). The cumulative survival rates in the experimental at 1-, 1.5-, and 2-year post-treatment were higher than the control group. The cumulative recurrence and metastasis rates in the experimental at 1.5-, and 2-year post-treatment were significantly lower than those in the control group (P < 0.05). In addition, there were no significant differences in treatment-related complications in the two groups.

Conclusions

The combined treatment of TACE and PMCT for advanced HCC is a safe, feasible, and effective treatment method, prolonging the survival time, and reducing the recurrence and metastasis rate, without increased toxic and side effects.

Multimodal Environmental Sensing Using AI & IoT Solutions: A Cognitive Sound Analysis Perspective

This study presents a novel audio compression technique, tailored for environmental monitoring within multi-modal data processing pipelines. Considering the crucial role that audio data play in environmental evaluations, particularly in contexts with extreme resource limitations, our strategy substantially decreases bit rates to facilitate efficient data transfer and storage. This is accomplished without undermining the accuracy necessary for trustworthy air pollution analysis while simultaneously minimizing processing expenses. More specifically, our approach fuses a Deep-Learning-based model, optimized for edge devices, along with a conventional coding schema for audio compression. Once transmitted to the cloud, the compressed data undergo a decoding process, leveraging vast cloud computing resources for accurate reconstruction and classification. The experimental results indicate that our approach leads to a relatively minor decrease in accuracy, even at notably low bit rates, and demonstrates strong robustness in identifying data from labels not included in our training dataset.

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.

Digital patient twins for personalized therapeutics and pharmaceutical manufacturing

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

An energy-aware heart disease prediction system using ESMO and optimal deep learning model for healthcare monitoring in IoT

The Internet of Things (IoT), which provides seamless connectivity between people and things, improves our quality of life. In the medical field, predictive analytics can help transform a reactive healthcare (HC) strategy into a proactive one. The HC industry embraces cutting-edge artificial intelligence and machine learning (ML) technologies. ML's area of deep learning has the revolutionary potential to reliably analyze massive volumes of data quickly, produce insightful revelations and solve challenging issues. This article proposes an energy-aware heart disease prediction (HDP) system based on enhanced spider monkey optimization (ESMO) and a weight-optimized neural network for an IoT-based HC environment. The proposed work consists of two essential phases: energy-efficient data transmission and HDP. In energy-efficient transmission, the cluster leaders are optimally selected using ESMO and the cluster formation is done based on Euclidean distance. In HDP, the patient data are collected from the dataset, and essential features are extracted. After that, the dimensionality reduction is carried out using the modified linear discriminant analysis approach to reduce over-fitting issues. Finally, the HDP uses the enhanced Archimedes weight-optimized deep neural network (EAWO-DNN). The simulation findings demonstrate that the proposed optimal clustering mechanism enhances the network's lifespan by consuming minimal energy compared to the existing techniques. Also, the proposed EAWO-DNN classifier achieves higher prediction accuracy, precision, recall and f-measure than the conventional methods for predicting heart disease in IoT.Communicated by Ramaswamy H. Sarma.

Exploring the Risks, Benefits, Advances, and Challenges in Internet Integration in Medicine With the Advent of 5G Technology: A Comprehensive Review

The integration of 5G technology in the healthcare sector is poised to bring about transformative changes, offering numerous advantages such as enhanced telemedicine services, expedited data transfer for medical records, improved remote surgery capabilities, real-time monitoring and diagnostics, advancements in wearable medical devices, and the potential for precision medicine. However, this technological shift is not without its concerns, including potential health implications related to 5G radiation exposure, heightened cybersecurity risks for medical devices and data systems, potential system failures due to technology dependence, and privacy issues linked to data breaches in healthcare. We are striking a balance between harnessing these benefits and addressing the associated risks. Achieving this equilibrium requires the establishment of a robust regulatory framework, ongoing research into the health impacts of 5G radiation, the implementation of stringent cybersecurity measures, education and training for healthcare professionals, and the development of ethical standards. The future of 5G in the medical field holds immense promise, but success depends on our ability to navigate this evolving landscape while prioritizing patient safety, privacy, and ethical practice.

Assessment Scales in Cerebral Palsy: A Comprehensive Review of Tools and Applications

Cerebral palsy (CP) is a complex neurological condition characterized by motor dysfunction affecting millions worldwide. This comprehensive review delves into the critical role of assessment in managing CP. Beginning with exploring its definition and background, we elucidate the diverse objectives of CP assessment, ranging from diagnosis and goal setting to research and epidemiology. We examine standard assessment scales and tools, discuss the challenges inherent in CP assessment, and highlight emerging trends, including integrating technology, personalized medicine, and neuroimaging. The applications of CP assessment in clinical diagnosis, treatment planning, research, and education are underscored. Recommendations for the future encompass standardization, interdisciplinary collaboration, research priorities, and professional training. In conclusion, we emphasize the importance of assessment as a compass guiding the care of individuals with CP, issuing a call to action for improved assessment practices to shape a brighter future for those affected by this condition.

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation

Medical devices (MDs) have been designed for monitoring the parameters of patients in many sectors. Nonetheless, despite being high-performing and reliable, they often turn out to be expensive and intrusive. In addition, MDs are almost exclusively used in controlled, hospital-based environments. Paving a path of technological innovation in the clinical field, a very active line of research is currently dealing with the possibility to rely on non-medical-graded low-cost devices, to develop unattended telemedicine (TM) solutions aimed at non-invasively gathering data, signals, and images. In this article, a TM solution is proposed for monitoring the heart rate (HR) of patients during sleep. A remote patient monitoring system (RPMS) featuring a smart belt equipped with pressure sensors for ballistocardiogram (BCG) signals sampling was deployed. A field trial was then conducted over a 2-month period on 24 volunteers, who also agreed to wear a finger pulse oximeter capable of producing a photoplethysmography (PPG) signal as the gold standard, to examine the feasibility of the solution via the estimation of HR values from the collected BCG signals. For this purpose, two of the highest-performing approaches for HR estimation from BCG signals, one algorithmic and the other based on a convolutional neural network (CNN), were retrieved from the literature and updated for a TM-related use case. Finally, HR estimation performances were assessed in terms of patient-wise mean absolute error (MAE). Results retrieved from the literature (controlled environment) outperformed those achieved in the experimentation (TM environment) by 29% (MAE = 4.24 vs. 5.46, algorithmic approach) and 52% (MAE = 2.32 vs. 3.54, CNN-based approach), respectively. Nonetheless, a low packet loss ratio, restrained elaboration time of the collected biomedical big data, low-cost deployment, and positive feedback from the users, demonstrate the robustness, reliability, and applicability of the proposed TM solution. In light of this, further steps will be planned to fulfill new targets, such as evaluation of respiratory rate (RR), and pattern assessment of the movement of the participants overnight.

A comprehensive systematic and bibliometric review of the IoT-based healthcare systems

In the healthcare sector, the growth in technology has had a huge effect. Besides, when introduced to the world of healthcare, the Internet of Things (IoT) will simplify the transition by helping physicians closely track their patients, allowing rapid recovery. Aged patients/people should be intensively checked, and their loved ones must be aware of their wellbeing periodically. Therefore, using IoT in healthcare will simplify the lives of physicians and patients alike. Hence, this study explored a comprehensive review of intelligent IoT-based embedded healthcare systems. The papers around intelligent IoT-based healthcare systems printed until Dec-2022 are studied, and some research lines are suggested for the upcoming researchers. Thus, this study's innovation will apply healthcare systems based on IoT to include certain strategies for the future deployment of new generations of IoT-based health technology. The findings revealed that IoT is beneficial for governments to strengthen society's health and economic relations. Besides, owing to novel functional principles, IoT needs modern safety infrastructure. This study is helpful for prevalent and useful electronic healthcare services, health experts, and clinicians.

Bluetooth-Based Healthcare Information and Medical Resource Management System

This paper presents a healthcare information and medical resource management platform utilizing wearable devices, physiological sensors, and an indoor positioning system (IPS). This platform provides medical healthcare information management based on the physiological information collected by wearable devices and Bluetooth data collectors. The Internet of Things (IoT) is constructed for this medical care purpose. The collected data are classified and used to monitor the status of patients in real time with a Secure MQTT mechanism. The measured physiological signals are also used for developing an IPS. When the patient is out of the safety zone, the IPS will send an alert message instantly by pushing the server to remind the caretaker, easing the caretaker's burden and offering extra protection for the patient. The presented system also provides medical resource management with the help of IPS. The medical equipment and devices can be tracked by IPS to tackle some equipment rental problems, such as lost and found. A platform for the medical staff work coordination information exchange and transmission is also developed to expedite the maintenance of medical equipment, providing the shared medical information to healthcare and management staff in a timely and transparent manner. The presented system in this paper will finally reduce the loading of medical staff during the COVID-19 pandemic period.

Evaluation of Novel Embroidered Textile-Electrodes Made from Hybrid Polyamide Conductive Threads for Surface EMG Sensing

Recently, there has been an increase in the number of reports on textile-based dry electrodes that can detect biopotentials without the need for electrolytic gels. However, these textile electrodes have a higher electrode skin interface impedance due to the improper contact between the skin and the electrode, diminishing the reliability and repeatability of the sensor. To facilitate improved skin-electrode contact, the effects of load and holding contact pressure were monitored for an embroidered textile electrode composed of multifilament hybrid thread for its application as a surface electromyography (sEMG) sensor. The effect of the textile's inter-electrode distance and double layering of embroidery that increases the density of the conductive threads were studied. Electrodes embroidered onto an elastic strap were wrapped around the forearm with a hook and loop fastener and tested for their performance. Time domain features such as the Root Mean Square (RMS), Average Rectified Value (ARV), and Signal to Noise Ratio (SNR) were quantitatively monitored in relation to the contact pressure and load. Experiments were performed in triplicates, and the sEMG signal characteristics were observed for various loads (0, 2, 4, and 6 kg) and holding contact pressures (5, 10, and 20 mmHg). sEMG signals recorded with textile electrodes were comparable in amplitude to those recorded using typical Ag/AgCl electrodes (28.45 dB recorded), while the signal-to-noise ratios were, 11.77, 19.60, 19.91, and 20.93 dB for the different loads, and 21.33, 23.34, and 17.45 dB for different holding pressures. The signal quality increased as the elastic strap was tightened further, but a pressure higher than 20 mmHg is not recommended because of the discomfort experienced by the subjects during data collection.

Utilization of 5G Technologies in IoT Applications: Current Limitations by Interference and Network Optimization Difficulties-A Review

5G (fifth-generation technology) technologies are becoming more mainstream thanks to great efforts from telecommunication companies, research facilities, and governments. This technology is often associated with the Internet of Things to improve the quality of life for citizens by automating and gathering data recollection processes. This paper presents the 5G and IoT technologies, explaining common architectures, typical IoT implementations, and recurring problems. This work also presents a detailed and explained overview of interference in general wireless applications, interference unique to 5G and IoT, and possible optimization techniques to overcome these challenges. This manuscript highlights the importance of addressing interference and optimizing network performance in 5G networks to ensure reliable and efficient connectivity for IoT devices, which is essential for adequately functioning business processes. This insight can be helpful for businesses that rely on these technologies to improve their productivity, reduce downtime, and enhance customer satisfaction. We also highlight the potential of the convergence of networks and services in increasing the availability and speed of access to the internet, enabling a range of new and innovative applications and services.

Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population

It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.

Amplifiers in Biomedical Engineering: A Review from Application Perspectives

Continuous monitoring and treatment of various diseases with biomedical technologies and wearable electronics has become significantly important. The healthcare area is an important, evolving field that, among other things, requires electronic and micro-electromechanical technologies. Designed circuits and smart devices can lead to reduced hospitalization time and hospitals equipped with high-quality equipment. Some of these devices can also be implanted inside the body. Recently, various implanted electronic devices for monitoring and diagnosing diseases have been presented. These instruments require communication links through wireless technologies. In the transmitters of these devices, power amplifiers are the most important components and their performance plays important roles. This paper is devoted to collecting and providing a comprehensive review on the various designed implanted amplifiers for advanced biomedical applications. The reported amplifiers vary with respect to the class/type of amplifier, implemented CMOS technology, frequency band, output power, and the overall efficiency of the designs. The purpose of the authors is to provide a general view of the available solutions, and any researcher can obtain suitable circuit designs that can be selected for their problem by reading this survey.

A Novel Low Complexity Two-Stage Tone Reservation Scheme for PAPR Reduction in OFDM Systems

Orthogonal frequency division multiplexing (OFDM) has the characteristics of high spectrum efficiency and excellent anti-multipath interference ability. It is the most popular and mature technology currently in wireless communication. However, OFDM is a multi-carrier system, which inevitably has the problem of a high peak-to-average power ratio (PAPR), and s signal with too high PAPR is prone to distortion when passing through an amplifier due to nonlinearity. To address the troubles caused by high PAPR, we proposed an improved tone reservation (I-TR) algorithm to alleviate the above native phenomenon, which will pay some modest pre-calculations to estimate the rough proportion of peak reduction tone (PRT) to determine the appropriate output power allocation threshold then utilize a few iterations to converge to the near-optimal PAPR. Furthermore, our proposed scheme significantly outperforms previous works in terms of PAPR performance and computational complexity, such as selective mapping (SLM), partial transmission sequence (PTS), TR, tone injection (TI), etc. The simulation results show that in our proposed scheme, the PAPR is appreciably reduced by about 6.44 dB compared with the original OFDM technique at complementary cumulative distribution function (CCDF) equal to 10-3, and the complexity of I-TR has reduced by approximately 96% compared to TR. Besides, as for bit error rate (BER), our proposed method always outperforms the original OFDM without any sacrifice.