Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

Innovations in blood pressure measurement and reporting technology: International Society of Hypertension position paper endorsed by the World Hypertension League, European Society of Hypertension, Asian Pacific Society of Hypertension, and Latin American Society of Hypertension

Blood pressure (BP) is a key contributor to the lifetime risk of preclinical organ damage and cardiovascular disease. Traditional clinic-based BP readings are typically measured infrequently and under standardized/resting conditions and therefore do not capture BP values during normal everyday activity. Therefore, current hypertension guidelines emphasize the importance of incorporating out-of-office BP measurement into strategies for hypertension diagnosis and management. However, conventional home and ambulatory BP monitoring devices use the upper-arm cuff oscillometric method and only provide intermittent BP readings under static conditions or in a limited number of situations. New innovations include technologies for BP estimation based on processing of sensor signals supported by artificial intelligence tools, technologies for remote monitoring, reporting and storage of BP data, and technologies for BP data interpretation and patient interaction designed to improve hypertension management ("digital therapeutics"). The number and volume of data relating to new devices/technologies is increasing rapidly and will continue to grow. This International Society of Hypertension position paper describes the new devices/technologies, presents evidence relating to new BP measurement techniques and related indices, highlights standard for the validation of new devices/technologies, discusses the reliability and utility of novel BP monitoring devices, the association of these metrics with clinical outcomes, and the use of digital therapeutics. It also highlights the challenges and evidence gaps that need to be overcome before these new technologies can be considered as a user-friendly and accurate source of novel BP data to inform clinical hypertension management strategies.

A 3D printed serrated contact structure triboelectric nanogenerator for swimming training safety monitoring

The wearable electronic devices integrated with 3D printing have attracted much attention, but the continuous power supply demand and limited application scenarios have limited their development. Here, we propose a 3D printed serrated contact structure triboelectric nanogenerator (S-TENG) designed for mechanical energy harvesting and swimming training safety monitoring. Leveraging the advancements in 3D printing technology, we created a flexible, lightweight sensor integrated with polytetrafluoroethylene (PTFE) and polyethylene terephthalate (PET) films on a serrated substrate. This configuration enhances the contact surface area, leading to a significant improvement in energy harvesting efficiency compared to flat structures. Specifically, the serrated structure resulted in a 64 %, 63 %, and 47 % increase in open-circuit voltage (Voc), short-circuit current (Isc), and transferred charge (Qsc), respectively, owing to the contact area and unique surface functional structure. The S-TENG device exhibits excellent performance under various bending angles, with Voc, Isc, and Qsc reaching up to 98.04 V, 4.35 μA, and 38.51 nC at 90° bending. Additionally, the S-TENG maintains stable output in different humidity environments due to its fully encapsulated design, ensuring reliable operation in aquatic settings. The S-TENG can accurately measure elbow swing amplitude and frequency, providing valuable real-time data for athletes and coaches. The S-TENG's ability to detect irregular movements and potential drowning incidents underscores its promise in enhancing swimmer safety. This research demonstrates the S-TENG's utility in both energy harvesting and motion monitoring, paving the way for advanced wearable sports sensors in various athletic disciplines.

Photoresist Development for 3D Printing of Conductive Microstructures via Two-Photon Polymerization

The advancement of electronic devices necessitates the development of three-dimensional (3D) high-precision conductive microstructures, which have extensive applications in bio-electronic interfaces, soft robots, and electronic skins. Two-photon polymerization (TPP) based 3D printing is a critical technique that offers unparalleled fabrication resolution in 3D space for intricate conductive structures. While substantial progress has been made in this field, this review summarizes recent advances in the 3D printing of conductive microstructures via TPP, mainly focusing on the essential criteria of photoresist resins suitable for TPP. Further preparation strategies of these photoresists and methods for constructing 3D conductive microstructures via TPP are discussed. The application prospects of 3D conductive microstructures in various fields are discussed, highlighting the imperative to advance their additive manufacturing technology. Finally, strategic recommendations are offered to enhance the construction of 3D conductive microstructures using TPP, addressing prevailing challenges and fostering significant advancements in manufacturing technology.

Validation and verification framework and data integration of biosensors and in vitro diagnostic devices: a position statement of the IFCC Committee on Mobile Health and Bioengineering in Laboratory Medicine (C-MBHLM) and the IFCC Scientific Division

Advances in technology have transformed healthcare and laboratory medicine. Biosensors have emerged as a promising technology in healthcare, providing a way to monitor human physiological parameters in a continuous, real-time, and non-intrusive manner and offering value and benefits in a wide range of applications. This position statement aims to present the current situation around biosensors, their perspectives and importantly the need to set the framework for their validation and safe use. The development of a qualification framework for biosensors should be conceptually adopted and extended to cover digitally measured biomarkers from biosensors for advancing healthcare and achieving more individualized patient management and better patient outcome.

Pharma 4.0: A deep dive top management commitment to successful Lean 4.0 implementation in Ghanaian pharma manufacturing sector

The primary aim of this study is to assess the significance of top management commitment in the context of Lean 4.0 implementation within the pharmaceutical manufacturing industry in Ghana. The study seeks to understand and evaluate the overall effectiveness and achievements associated with adopting Lean 4.0. Employing a positivist mindset, the research utilizes an explanatory quantitative research design and a survey technique. Data collected from 181 employees of pharmaceutical companies in Ghana undergo analysis using SmartPLS (version 4) and IBM SPSS version 26. The study employs a combination of descriptive statistics to summarise data characteristics and inferential statistics to test various hypotheses related to Lean 4.0 adoption. The analysis reveals that the successful integration of lean methods and Industry 4.0 technologies requires meticulous management. Simultaneously, individual implementations of lean principles and Industry 4.0 technologies positively impact business performance. Surprisingly, the study does not observe a substantial positive influence of Lean 4.0 on corporate performance, suggesting that immediate improvements in efficiency or profitability may not result from the adoption of this framework. This research contributes to the field by highlighting the need for careful management in integrating lean methods and Industry 4.0 technologies. It also emphasizes the positive impacts of lean principles and Industry 4.0 technology on business performance. The unexpected finding regarding the lack of immediate improvements in corporate efficiency or profitability with Lean 4.0 adoption prompts considerations of initial implementation challenges or the organization's need for time to adapt to this integrated approach.

Unveiling wearables: exploring the global landscape of biometric applications and vital signs and behavioral impact

The development of neuroscientific techniques enabling the recording of brain and peripheral nervous system activity has fueled research in cognitive science. Recent technological advancements offer new possibilities for inducing behavioral change, particularly through cost-effective Internet-based interventions. However, limitations in laboratory equipment volume have hindered the generalization of results to real-life contexts. The advent of Internet of Things (IoT) devices, such as wearables, equipped with sensors and microchips, has ushered in a new era in behavior change techniques. Wearables, including smartwatches, electronic tattoos, and more, are poised for massive adoption, with an expected annual growth rate of 55% over the next five years. These devices enable personalized instructions, leading to increased productivity and efficiency, particularly in industrial production. Additionally, the healthcare sector has seen a significant demand for wearables, with over 80% of global consumers willing to use them for health monitoring. This research explores the primary biometric applications of wearables and their impact on users' well-being, focusing on the integration of behavior change techniques facilitated by IoT devices. Wearables have revolutionized health monitoring by providing real-time feedback, personalized interventions, and gamification. They encourage positive behavior changes by delivering immediate feedback, tailored recommendations, and gamified experiences, leading to sustained improvements in health. Furthermore, wearables seamlessly integrate with digital platforms, enhancing their impact through social support and connectivity. However, privacy and data security concerns must be addressed to maintain users' trust. As technology continues to advance, the refinement of IoT devices' design and functionality is crucial for promoting behavior change and improving health outcomes. This study aims to investigate the effects of behavior change techniques facilitated by wearables on individuals' health outcomes and the role of wearables in promoting a healthier lifestyle.

Cross-Attention Enhanced Pyramid Multi-Scale Networks for Sensor-Based Human Activity Recognition

Human Activity Recognition (HAR) has recently attracted widespread attention, with the effective application of this technology helping people in areas such as healthcare, smart homes, and gait analysis. Deep learning methods have shown remarkable performance in HAR. A pivotal challenge is the trade-off between recognition accuracy and computational efficiency, especially in resource-constrained mobile devices. This challenge necessitates the development of models that enhance feature representation capabilities without imposing additional computational burdens. Addressing this, we introduce a novel HAR model leveraging deep learning, ingeniously designed to navigate the accuracy-efficiency trade-off. The model comprises two innovative modules: 1) Pyramid Multi-scale Convolutional Network (PMCN), which is designed with a symmetric structure and is capable of obtaining a rich receptive field at a finer level through its multiscale representation capability; 2) Cross-Attention Mechanism, which establishes interrelationships among sensor dimensions, temporal dimensions, and channel dimensions, and effectively enhances useful information while suppressing irrelevant data. The proposed model is rigorously evaluated across four diverse datasets: UCI, WISDM, PAMAP2, and OPPORTUNITY. Additional ablation and comparative studies are conducted to comprehensively assess the performance of the model. Experimental results demonstrate that the proposed model achieves superior activity recognition accuracy while maintaining low computational overhead.

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.

Clinical application of a body area network-based smart bracelet for pre-hospital trauma care

Objective

This study aims to explore the efficiency and effectiveness of a body area network-based smart bracelet for trauma care prior to hospitalization.

Methods

To test the efficacy of the bracelet, an observational cohort study was conducted on the clinical data of 140 trauma patients pre-admission to the hospital. This study was divided into an experimental group receiving smart bracelets and a control group receiving conventional treatment. Both groups were randomized using a random number table. The primary variables of this study were as follows: time to first administration of life-saving intervention, time to first administration of blood transfusion, time to first administration of hemostatic drugs, and mortality rates within 24 h and 28 days post-admission to the hospital. The secondary outcomes included the amount of time before trauma team activation and the overall length of patient stay in the emergency room.

Results

The measurement results for both the emergency smart bracelet as well as traditional equipment showed high levels of consistency and accuracy. In terms of pre-hospital emergency life-saving intervention, there was no significant statistical difference in the mortality rates between both groups within 224 h post-admission to the hospital or after 28-days of treatment in the emergency department. Furthermore, the treatment efficiency for the group of patients wearing smart bracelets was significantly better than that of the control group with regard to both the primary and secondary outcomes of this study. These results indicate that this smart bracelet has the potential to improve the efficiency and effectiveness of trauma care and treatment.

Conclusion

A body area network-based smart bracelet combined with remote 5G technology can assist the administration of emergency care to trauma patients prior to hospital admission, shorten the timeframe in which life-saving interventions are initiated, and allow for a quick trauma team response as well as increased efficiency upon administration of emergency care.

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

Colombian Stakeholder Perceptions and Recommendations Regarding Fall Detection Systems for Older Adults

This study aimed to analyze perceptions and recommendations from stakeholders on the effectiveness of fall detection systems for older adults, aside from any additional technological solutions they may use within their activities of daily living (ADLs). This study performed a mixed-method approach to explore the views and recommendations of stakeholders concerning the implementation of wearable fall detection systems. Semi-structured online interviews and surveys were conducted on 25 Colombian adults classified into four stakeholder groups: older adults, informal caregivers, healthcare professionals, and researchers. A total of 25 individuals were interviewed or surveyed, comprising 12 females (48%) and 13 males (52%). The four groups cited the importance of wearable fall detection systems in ADLs monitoring of older adults. They did not consider them stigmatizing nor discriminatory but some raised potential privacy issues. The groups also communicated that the apparatus could be small, lightweight, and easy to handle with a help message sent to a relative or caregiver. All stakeholders interviewed perceived assistive technology as potentially useful for opportune healthcare, as well as for promoting independent living for the end user and their family members. For this reason, this study assessed the perceptions and recommendations received concerning fall detectors depending on the needs of stakeholders and the settings in which they are used.

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.

Comparison between Two Time Synchronization and Data Alignment Methods for Multi-Channel Wearable Biosensor Systems Using BLE Protocol

Wireless wearable sensor systems for biomedical signal acquisition have developed rapidly in recent years. Multiple sensors are often deployed for monitoring common bioelectric signals, such as EEG (electroencephalogram), ECG (electrocardiogram), and EMG (electromyogram). Compared with ZigBee and low-power Wi-Fi, Bluetooth Low Energy (BLE) can be a more suitable wireless protocol for such systems. However, current time synchronization methods for BLE multi-channel systems, via either BLE beacon transmissions or additional hardware, cannot satisfy the requirements of high throughput with low latency, transferability between commercial devices, and low energy consumption. We developed a time synchronization and simple data alignment (SDA) algorithm, which was implemented in the BLE application layer without the need for additional hardware. We further developed a linear interpolation data alignment (LIDA) algorithm to improve upon SDA. We tested our algorithms using sinusoidal input signals at different frequencies (10 to 210 Hz in increments of 20 Hz-frequencies spanning much of the relevant range of EEG, ECG, and EMG signals) on Texas Instruments (TI) CC26XX family devices, with two peripheral nodes communicating with one central node. The analysis was performed offline. The lowest average (±standard deviation) absolute time alignment error between the two peripheral nodes achieved by the SDA algorithm was 384.3 ± 386.5 μs, while that of the LIDA algorithm was 189.9 ± 204.7 μs. For all sinusoidal frequencies tested, the performance of LIDA was always statistically better than that of SDA. These average alignment errors were quite low-well below one sample period for commonly acquired bioelectric signals.

Smart Graphene-Based Electrochemical Nanobiosensor for Clinical Diagnosis: Review

The technological improvement in the field of physics, chemistry, electronics, nanotechnology, biology, and molecular biology has contributed to the development of various electrochemical biosensors with a broad range of applications in healthcare settings, food control and monitoring, and environmental monitoring. In the past, conventional biosensors that have employed bioreceptors, such as enzymes, antibodies, Nucleic Acid (NA), etc., and used different transduction methods such as optical, thermal, electrochemical, electrical and magnetic detection, have been developed. Yet, with all the progresses made so far, these biosensors are clouded with many challenges, such as interference with undesirable compound, low sensitivity, specificity, selectivity, and longer processing time. In order to address these challenges, there is high need for developing novel, fast, highly sensitive biosensors with high accuracy and specificity. Scientists explore these gaps by incorporating nanoparticles (NPs) and nanocomposites (NCs) to enhance the desired properties. Graphene nanostructures have emerged as one of the ideal materials for biosensing technology due to their excellent dispersity, ease of functionalization, physiochemical properties, optical properties, good electrical conductivity, etc. The Integration of the Internet of Medical Things (IoMT) in the development of biosensors has the potential to improve diagnosis and treatment of diseases through early diagnosis and on time monitoring. The outcome of this comprehensive review will be useful to understand the significant role of graphene-based electrochemical biosensor integrated with Artificial Intelligence AI and IoMT for clinical diagnostics. The review is further extended to cover open research issues and future aspects of biosensing technology for diagnosis and management of clinical diseases and performance evaluation based on Linear Range (LR) and Limit of Detection (LOD) within the ranges of Micromolar µM (10^-6), Nanomolar nM (10^-9), Picomolar pM (10^-12), femtomolar fM (10^-15), and attomolar aM (10^-18).

Healthcare Monitoring Using Low-Cost Sensors to Supplement and Replace Human Sensation: Does It Have Potential to Increase Independent Living and Prevent Disease?

Continuous monitoring of health status has the potential to enhance the quality of life and life expectancy of people suffering from chronic illness and of the elderly. However, such systems can only come into widespread use if the cost of manufacturing is low. Advancements in material science and engineering technology have led to a significant decrease in the expense of developing healthcare monitoring devices. This review aims to investigate the progress of the use of low-cost sensors in healthcare monitoring and discusses the challenges faced when accomplishing continuous and real-time monitoring tasks. The major findings include (1) only a small number of publications (N = 50) have addressed the issue of healthcare monitoring applications using low-cost sensors over the past two decades; (2) the top three algorithms used to process sensor data include SA (Statistical Analysis, 30%), SVM (Support Vector Machine, 18%), and KNN (K-Nearest Neighbour, 12%); and (3) wireless communication techniques (Zigbee, Bluetooth, Wi-Fi, and RF) serve as the major data transmission tools (77%) followed by cable connection (13%) and SD card data storage (10%). Due to the small fraction (N = 50) of low-cost sensor-based studies among thousands of published articles about healthcare monitoring, this review not only summarises the progress of related research but calls for researchers to devote more effort to the consideration of cost reduction as well as the size of these components.

Scalable Fabrication of MXene-PVDF Nanocomposite Triboelectric Fibers via Thermal Drawing

In the data-driven world, textile is a valuable resource for improving the quality of life through continuous monitoring of daily activities and physiological signals of humans. Triboelectric nanogenerators (TENG) are an attractive option for self-powered sensor development by coupling energy harvesting and sensing ability. In this study, to the best of the knowledge, scalable fabrication of Ti₃ C₂ T_x MXene-embedded polyvinylidene fluoride (PVDF) nanocomposite fiber using a thermal drawing process is presented for the first time. The output open circuit voltage and short circuit current show 53% and 58% improvement, respectively, compared to pristine PVDF fiber. The synergistic interaction between the surface termination groups of MXene and polar PVDF polymer enhances the performance of the fiber. The flexibility of the fiber enables the weaving of fabric TENG devices for large-area applications. The fabric TENG (3 × 2 cm² ) demonstrates a power density of 40.8 mW m^-2 at the matching load of 8 MΩ by maintaining a stable performance over 12 000 cycles. Moreover, the fabric TENG has shown the capability of energy harvesting by operating a digital clock and a calculator. A distributed self-powered sensor for human activities and walking pattern monitoring are demonstrated with the fabric.

Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification

Wearable devices are rapidly spreading thanks to multiple advantages. Their use is expanding in several fields, from medicine to personal assessment and sport applications. At present, more and more wearable devices acquire an electrocardiographic (ECG) signal (in correspondence to the wrist), providing potentially useful information from a diagnostic point of view, particularly in sport medicine and in rehabilitation fields. They are remarkably relevant, being perceived as a common watch and, hence, considered neither intrusive nor a cause of the so-called "white coat effect". Their validation and metrological characterization are fundamental; hence, this work aims at defining a validation protocol tested on a commercial smartwatch (Samsung Galaxy Watch3, Samsung Electronics Italia S.p.A., Milan, Italy) with respect to a gold standard device (Zephyr BioHarness 3.0, Zephyr Technology Corporation, Annapolis, MD, USA, accuracy of ±1 bpm), reporting results on 30 subjects. The metrological performance is provided, supporting final users to properly interpret the results. Moreover, machine learning and deep learning models are used to discriminate between resting and activity-related ECG signals. The results confirm the possibility of using heart rate data from wearable sensors for activity identification (best results obtained by Random Forest, with accuracy of 0.81, recall of 0.80, and precision of 0.81, even using ECG signals of limited duration, i.e., 30 s). Moreover, the effectiveness of the proposed validation protocol to evaluate measurement accuracy and precision in a wide measurement range is verified. A bias of -1 bpm and an experimental standard deviation of 11 bpm (corresponding to an experimental standard deviation of the mean of ≈0 bpm) were found for the Samsung Galaxy Watch3, indicating a good performance from a metrological point of view.

Development of an Intelligent System for the Monitoring and Diagnosis of the Well-Being

Today, society is more aware of their well-being and health, making wearable devices a new and affordable way to track them continuously. Smartwatches allow access to daily vital physiological measurements, which help people to be aware of their health status. Even though these technologies allow the following of different health conditions, their application in health is still limited to the following physical parameters to allow physicians treatment and diagnosis. This paper presents LM Research, a smart monitoring system mainly composed of a web page, REST APIs, machine learning algorithms, psychological questionnaire, and smartwatches. The system introduces the continuous monitoring of the users' physical and mental indicators to prevent a wellness crisis; the mental indicators and the system's continuous feedback to the user could be, in the future, a tool for medical specialists treating well-being. For this purpose, it collects psychological parameters on smartwatches and mental health data using a psychological questionnaire to develop a supervised machine learning wellness model that predicts the wellness of smartwatch users. The full construction of the database and the technology employed for its development is presented. Moreover, six machine learning algorithms (Decision Tree, Random Forest, Naive Bayes, Neural Networks, Support Vector Machine, and K-nearest neighbor) were applied to the database to test which classifies better the information obtained by the proposed system. In order to integrate this algorithm into LM Research, Random Forest being the one with the higher accuracy of 88%.

Internet of Things-Based ECG and Vitals Healthcare Monitoring System

Health monitoring and its associated technologies have gained enormous importance over the past few years. The electrocardiogram (ECG) has long been a popular tool for assessing and diagnosing cardiovascular diseases (CVDs). Since the literature on ECG monitoring devices is growing at an exponential rate, it is becoming difficult for researchers and healthcare professionals to select, compare, and assess the systems that meet their demands while also meeting the monitoring standards. This emphasizes the necessity for a reliable reference to guide the design, categorization, and analysis of ECG monitoring systems, which will benefit both academics and practitioners. We present a complete ECG monitoring system in this work, describing the design stages and implementation of an end-to-end solution for capturing and displaying the patient's heart signals, heart rate, blood oxygen levels, and body temperature. The data will be presented on an OLED display, a developed Android application as well as in MATLAB via serial communication. The Internet of Things (IoT) approaches have a clear advantage in tackling the problem of heart disease patient care as they can transform the service mode into a widespread one and alert the healthcare services based on the patient's physical condition. Keeping this in mind, there is also the addition of a web server for monitoring the patient's status via WiFi. The prototype, which is compliant with the electrical safety regulations and medical equipment design, was further benchmarked against a commercially available off-the-shelf device, and showed an excellent accuracy of 99.56%.

Gold Nanoclusters Dispersed on Gold Dendrite-Based Carbon Fibre Microelectrodes for the Sensitive Detection of Nitric Oxide in Human Serum

Herein, gold nanoclusters (Au NC) dispersed on gold dendrite (Au DS)-based flexible carbon fibre (AuNC@AuDS|CF) microelectrodes are developed using a one-step electrochemical approach. The as-fabricated AuNC@AuDS|CF microelectrodes work as the prospective electrode materials for the sensitive detection of nitric oxide (NO) in a 0.1 M phosphate buffer (PB) solution. Carbon microfibre acts as an efficient matrix for the direct growth of AuNC@AuDS without any binder/extra reductant. The AuNC@AuDS|CF microelectrodes exhibit outstanding electrocatalytic activity towards NO oxidation, which is ascribed to their large electrochemical active surface area (ECSA), high electrical conductivity, and high dispersion of Au nanoclusters. As a result, the AuNC@AuDS|CF microelectrodes attain a rapid response time (3 s), a low limit of detection (LOD) (0.11 nM), high sensitivity (66.32 µA µM cm^-2), a wide linear range (2 nM-7.7 µM), long-term stability, good reproducibility, and a strong anti-interference capability. Moreover, the present microsensor successfully tested for the discriminating detection of NO in real human serum samples, revealing its potential practicability.

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

The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real-time remote patient monitoring to improve the quality of people's lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. We provide a systematic review on recent studies of IoT-based healthcare-monitoring systems through literature review. The literature review compares various systems' effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare-monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends.

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning

The Internet of Things confers seamless connectivity between people and objects, and its confluence with the Cloud improves our lives. Predictive analytics in the medical domain can help turn a reactive healthcare strategy into a proactive one, with advanced artificial intelligence and machine learning approaches permeating the healthcare industry. As the subfield of ML, deep learning possesses the transformative potential for accurately analysing vast data at exceptional speeds, eliciting intelligent insights, and efficiently solving intricate issues. The accurate and timely prediction of diseases is crucial in ensuring preventive care alongside early intervention for people at risk. With the widespread adoption of electronic clinical records, creating prediction models with enhanced accuracy is key to harnessing recurrent neural network variants of deep learning possessing the ability to manage sequential time-series data. The proposed system acquires data from IoT devices, and the electronic clinical data stored on the cloud pertaining to patient history are subjected to predictive analytics. The smart healthcare system for monitoring and accurately predicting heart disease risk built around Bi-LSTM (bidirectional long short-term memory) showcases an accuracy of 98.86%, a precision of 98.9%, a sensitivity of 98.8%, a specificity of 98.89%, and an F-measure of 98.86%, which are much better than the existing smart heart disease prediction systems.

Wearable Sensors for Healthcare: Fabrication to Application

This paper presents a substantial review of the deployment of wearable sensors for healthcare applications. Wearable sensors hold a pivotal position in the microelectronics industry due to their role in monitoring physiological movements and signals. Sensors designed and developed using a wide range of fabrication techniques have been integrated with communication modules for transceiving signals. This paper highlights the entire chronology of wearable sensors in the biomedical sector, starting from their fabrication in a controlled environment to their integration with signal-conditioning circuits for application purposes. It also highlights sensing products that are currently available on the market for a comparative study of their performances. The conjugation of the sensing prototypes with the Internet of Things (IoT) for forming fully functioning sensorized systems is also shown here. Finally, some of the challenges existing within the current wearable systems are shown, along with possible remedies.

Textile-type triboelectric nanogenerator using Teflon wrapping wires as wearable power source

Wearable electronic devices such as mobile communication devices, portable computers, and various sensors are the latest significant innovations in technology which use the Internet of Things (IoT) to track personal data. Wearable energy harvesters are required to supply electricity to such devices for the convenience of users. In this study, a textile-type triboelectric nanogenerator (T-TENG), produced using commercial electrode fibers, was fabricated to generate electrical energy using external mechanical stimulation. The commercial fiber was an electrode coated with Teflon on a copper wire with a diameter of ~ 320 μm. Using this commercial fiber, a T-TENG was easily fabricated by knitting and weaving. The performance of the T-TENG was analyzed to understand the effect of force and frequency. It was observed that the performance of the T-TENG did not degrade even under harsh conditions and treatment. The textile-type TENG possessed an energy harvesting capability with an output power density of ~ 0.36 W/m2 and could operate electronic devices by charging a capacitor.

Development and Characterization of Embroidery-Based Textile Electrodes for Surface EMG Detection

The interest in wearable devices has expanded to measurement devices for building IoT-based mobile healthcare systems and sensing bio-signal data through clothing. Surface electromyography, called sEMG, is one of the most popular bio-signals that can be applied to health monitoring systems. In general, gel-based (Ag/AgCl) electrodes are mainly used, but there are problems, such as skin irritation due to long-time wearing, deterioration of adhesion to the skin due to moisture or sweat, and low applicability to clothes. Hence, research on dry electrodes as a replacement is increasing. Accordingly, in this study, a textile-based electrode was produced with a range of electrode shapes, and areas were embroidered with conductive yarn using an embroidery technique in the clothing manufacturing process. The electrode was applied to EMG smart clothing for fitness, and the EMG signal detection performance was analyzed. The electrode shape was manufactured using the circle and wave type. The wave-type electrode was more morphologically stable than the circle-type electrode by up to 30% strain, and the electrode shape was maintained as the embroidered area increased. Skin-electrode impedance analysis confirmed that the embroidered area with conductive yarn affected the skin contact area, and the impedance decreased with increasing area. For sEMG performance analysis, the rectus femoris was selected as a target muscle, and the sEMG parameters were analyzed. The wave-type sample showed higher EMG signal strength than the circle-type. In particular, the electrode with three lines showed better performance than the fill-type electrode. These performances operated without noise, even with a commercial device. Therefore, it is expected to be applicable to the manufacture of electromyography smart clothing based on embroidered electrodes in the future.

Green syntheses of graphene and its applications in internet of things (IoT)-a status review

Internet of Things (IoT) is a trending technological field that converts any physical object into a communicable smarter one by converging the physical world with the digital world. This innovative technology connects the device to the internet and provides a platform to collect real-time data, cloud storage, and analyze the collected data to trigger smart actions from a remote location via remote notifications, etc. Because of its wide-ranging applications, this technology can be integrated into almost all the industries. Another trending field with tremendous opportunities is Nanotechnology, which provides many benefits in several areas of life, and helps to improve many technological and industrial sectors. So, integration of IoT and Nanotechnology can bring about the very important field of Internet of Nanothings (IoNT), which can re-shape the communication industry. For that, data (collected from trillions of nanosensors, connected to billions of devices) would be the 'ultimate truth', which could be generated from highly efficient nanosensors, fabricated from various novel nanomaterials, one of which is graphene, the so-called 'wonder material' of the 21st century. Therefore, graphene-assisted IoT/IoNT platforms may revolutionize the communication technologies around the globe. In this article, a status review of the smart applications of graphene in the IoT sector is presented. Firstly, various green synthesis of graphene for sustainable development is elucidated, followed by its applications in various nanosensors, detectors, actuators, memory, and nano-communication devices. Also, the future market prospects are discussed to converge various emerging concepts like machine learning, fog/edge computing, artificial intelligence, big data, and blockchain, with the graphene-assisted IoT field to bring about the concept of 'all-round connectivity in every sphere possible'.

Self-Oxygen Regulator System for COVID-19 Patients Based on Body Weight, Respiration Rate, and Blood Saturation

One of the symptoms that appears in patients with COVID-19 is hypoxia or a lack of oxygen in the body’s tissues or cells below the proper level. One of the methods used to treat hypoxia is to provide oxygen to the patient. Another device that is needed in oxygen therapy for the patient is an oxygen regulator. An oxygen regulator is needed to regulate the volume of oxygen released to the patient. Currently, the control of oxygen flow by the regulator is still done manually. Therefore, in this study, an oxygen regulator was designed that has the ability to regulate the volume of oxygen output based on body weight, respiration rate, and blood saturation. Using these three parameters, the volume of oxygen to be released is adjusted according to the patient’s needs. The system consists of a temperature sensor, mlx90614, and an oxygen saturation sensor, Max30102. The data from the two sensors are processed using microcontrollers to control the movement of the stepper motor as a regulator of the oxygen output volume. The test results show that the system can control the oxygen regulator automatically with a delta error of 0.5–1 L/min. This device is expected to be used for COVID-19 patients who are undergoing self-isolation or who are outpatients.

Smart Electronic Textiles for Wearable Sensing and Display

Increasing demand of using everyday clothing in wearable sensing and display has synergistically advanced the field of electronic textiles, or e-textiles. A variety of types of e-textiles have been formed into stretchy fabrics in a manner that can maintain their intrinsic properties of stretchability, breathability, and wearability to fit comfortably across different sizes and shapes of the human body. These unique features have been leveraged to ensure accuracy in capturing physical, chemical, and electrophysiological signals from the skin under ambulatory conditions, while also displaying the sensing data or other immediate information in daily life. Here, we review the emerging trends and recent advances in e-textiles in wearable sensing and display, with a focus on their materials, constructions, and implementations. We also describe perspectives on the remaining challenges of e-textiles to guide future research directions toward wider adoption in practice.

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Today’s advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.

A Versatile and Ubiquitous IoT-Based Smart Metabolic and Immune Monitoring System

In the present medical age, the focus on prevention and prediction is achieved using the medical internet of things. With a broad and complete framework, effective behavioral, environmental, and physiological criteria are necessary to govern the major healthcare sectors. Wearables play an essential role in personal health monitoring data measurement and processing. We wish to design a variable and flexible frame for broad parameter monitoring in accordance with the convenient mode of wearability. In this study, an innovative prototype with a handle and a modular IoT portal is designed for environmental surveillance. The prototype examines the most significant parameters of the surroundings. This strategy allows a bidirectional link between end users and medicine via the IoT gateway as an intermediate portal for users with IoT servers in real time. In addition, the doctor may configure the necessary parameters of measurements via the IoT portal and switch the sensors on the wearables as a real-time observer for the patient. Thus, based on goal analysis, patient situation, specifications, and requests, medications may define setup criteria for calculation. With regard to privacy, power use, and computation delays, we established this system's performance link for three common IoT healthcare circumstances. The simulation results show that this technique may minimize processing time by 25.34%, save energy level up to 72.25%, and boost the privacy level of the IoT medical device to 17.25% compared to the benchmark system.

Redundancy cancellation of compressed measurements by QRS complex alignment

The demand for long-term continuous care has led healthcare experts to focus on development challenges. On-chip energy consumption as a key challenge can be addressed by data reduction techniques. In this paper, the pseudo periodic nature of ElectroCardioGram(ECG) signals has been used to completely remove redundancy from frames. Compressing aligned QRS complexes by Compressed Sensing (CS), result in highly redundant measurement vectors. By removing this redundancy, a high cluster of near zero samples is gained. The efficiency of the proposed algorithm is assessed using the standard MIT-BIH database. The results indicate that by aligning ECG frames, the proposed technique can achieve superior reconstruction quality compared to state-of-the-art techniques for all compression ratios. This study proves that by aligning ECG frames with a 0.05% unaligned frame rate(R-peak detection error), more compression could be gained for PRD > 5% when 5-bit non-uniform quantizer is used. Furthermore, analysis done on power consumption of the proposed technique, indicates that a very good recovery performance can be gained by only consuming 4.9μW more energy per frame compared to traditional CS.

A Contemporary Review on Utilizing Semantic Web Technologies in Healthcare, Virtual Communities, and Ontology-Based Information Processing Systems

The semantic web is an emerging technology that helps to connect different users to create their content and also facilitates the way of representing information in a manner that can be made understandable for computers. As the world is heading towards the fourth industrial revolution, the implicit utilization of artificial-intelligence-enabled semantic web technologies paves the way for many real-time application developments. The fundamental building blocks for the overwhelming utilization of semantic web technologies are ontologies, and it allows sharing as well as reusing the concepts in a standardized way so that the data gathered from heterogeneous sources receive a common nomenclature, and it paves the way for disambiguating the duplicates very easily. In this context, the right utilization of ontology capabilities would further strengthen its presence in many web-based applications such as e-learning, virtual communities, social media sites, healthcare, agriculture, etc. In this paper, we have given the comprehensive review of using the semantic web in the domain of healthcare, some virtual communities, and other information retrieval projects. As the role of semantic web is becoming pervasive in many domains, the demand for the semantic web in healthcare, virtual communities, and information retrieval has been gaining huge momentum in recent years. To obtain the correct sense of the meaning of the words or terms given in the textual content, it is deemed necessary to apply the right ontology to fix the ambiguity and shun any deviations that persist on the concepts. In this review paper, we have highlighted all the necessary information for a good understanding of the semantic web and its ontological frameworks.

A Comprehensive Review of Internet of Things: Technology Stack, Middlewares, and Fog/Edge Computing Interface

The Internet of Things (IoT) is an extensive network of heterogeneous devices that provides an array of innovative applications and services. IoT networks enable the integration of data and services to seamlessly interconnect the cyber and physical systems. However, the heterogeneity of devices, underlying technologies and lack of standardization pose critical challenges in this domain. On account of these challenges, this research article aims to provide a comprehensive overview of the enabling technologies and standards that build up the IoT technology stack. First, a layered architecture approach is presented where the state-of-the-art research and open challenges are discussed at every layer. Next, this research article focuses on the role of middleware platforms in IoT application development and integration. Furthermore, this article addresses the open challenges and provides comprehensive steps towards IoT stack optimization. Finally, the interfacing of Fog/Edge Networks to IoT technology stack is thoroughly investigated by discussing the current research and open challenges in this domain. The main scope of this study is to provide a comprehensive review into IoT technology (the horizontal fabric), the associated middleware and networks required to build future proof applications (the vertical markets).

A Comprehensive Survey on the Internet of Things with the Industrial Marketplace

There is no doubt that new technology has become one of the crucial parts of most people's lives around the world. By and large, in this era, the Internet and the Internet of Things (IoT) have become the most indispensable parts of our lives. Recently, IoT technologies have been regarded as the most broadly used tools among other technologies. The tools and the facilities of IoT technologies within the marketplace are part of Industry 4.0. The marketplace is too regarded as a new area that can be used with IoT technologies. One of the main purposes of this paper is to highlight using IoT technologies in Industry 4.0, and the Industrial Internet of Things (IIoT) is another feature revised. This paper focuses on the value of the IoT in the industrial domain in general; it reviews the IoT and focuses on its benefits and drawbacks, and presents some of the IoT applications, such as in transportation and healthcare. In addition, the trends and facts that are related to the IoT technologies on the marketplace are reviewed. Finally, the role of IoT in telemedicine and healthcare and the benefits of IoT technologies for COVID-19 are presented as well.

The status and perspectives of nanostructured materials and fabrication processes for wearable piezoresistive sensors

The wearable sensors have attracted a growing interest in different markets, including health, fitness, gaming, and entertainment, due to their outstanding characteristics of convenience, simplicity, accuracy, speed, and competitive price. The development of different types of wearable sensors was only possible due to advances in smart nanostructured materials with properties to detect changes in temperature, touch, pressure, movement, and humidity. Among the various sensing nanomaterials used in wearable sensors, the piezoresistive type has been extensively investigated and their potential have been demonstrated for different applications. In this review article, the current status and challenges of nanomaterials and fabrication processes for wearable piezoresistive sensors are presented in three parts. The first part focuses on the different types of sensing nanomaterials, namely, zero-dimensional (0D), one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) piezoresistive nanomaterials. Then, in second part, their fabrication processes and integration are discussed. Finally, the last part presents examples of wearable piezoresistive sensors and their applications.

Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies

Cloud computing has become integral lately due to the ever-expanding Internet-of-things (IoT) network. It still is and continues to be the best practice for implementing complex computational applications, emphasizing the massive processing of data. However, the cloud falls short due to the critical constraints of novel IoT applications generating vast data, which entails a swift response time with improved privacy. The newest drift is moving computational and storage resources to the edge of the network, involving a decentralized distributed architecture. The data processing and analytics perform at proximity to end-users, and overcome the bottleneck of cloud computing. The trend of deploying machine learning (ML) at the network edge to enhance computing applications and services has gained momentum lately, specifically to reduce latency and energy consumed while optimizing the security and management of resources. There is a need for rigorous research efforts oriented towards developing and implementing machine learning algorithms that deliver the best results in terms of speed, accuracy, storage, and security, with low power consumption. This extensive survey presented on the prominent computing paradigms in practice highlights the latest innovations resulting from the fusion between ML and the evolving computing paradigms and discusses the underlying open research challenges and future prospects.

Improving the Classification of Alzheimer's Disease Using Hybrid Gene Selection Pipeline and Deep Learning

Alzheimer’s is a progressive, irreversible, neurodegenerative brain disease. Even with prominent symptoms, it takes years to notice, decode, and reveal Alzheimer’s. However, advancements in technologies, such as imaging techniques, help in early diagnosis. Still, sometimes the results are inaccurate, which delays the treatment. Thus, the research in recent times focused on identifying the molecular biomarkers that differentiate the genotype and phenotype characteristics. However, the gene expression dataset’s generated features are huge, 1,000 or even more than 10,000. To overcome such a curse of dimensionality, feature selection techniques are introduced. We designed a gene selection pipeline combining a filter, wrapper, and unsupervised method to select the relevant genes. We combined the minimum Redundancy and maximum Relevance (mRmR), Wrapper-based Particle Swarm Optimization (WPSO), and Auto encoder to select the relevant features. We used the GSE5281 Alzheimer’s dataset from the Gene Expression Omnibus We implemented an Improved Deep Belief Network (IDBN) with simple stopping criteria after choosing the relevant genes. We used a Bayesian Optimization technique to tune the hyperparameters in the Improved Deep Belief Network. The tabulated results show that the proposed pipeline shows promising results.