An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things

The health care industry has faced various challenges over the past decade as we move toward a digital future where services and data are available on demand. The systems of interconnected devices, users, data, and working environments are referred to as the Internet of Health Care Things (IoHT). IoHT devices have emerged in the past decade as cost-effective solutions with large scalability capabilities to address the constraints on limited resources. These devices cater to the need for remote health care services outside of physical interactions. However, IoHT security is often overlooked because the devices are quickly deployed and configured as solutions to meet the demands of a heavily saturated industry. During the COVID-19 pandemic, studies have shown that cybercriminals are exploiting the health care industry, and data breaches are targeting user credentials through authentication vulnerabilities. Poor password use and management and the lack of multifactor authentication security posture within IoHT cause a loss of millions according to the IBM reports. Therefore, it is important that health care authentication security moves toward adaptive multifactor authentication (AMFA) to replace the traditional approaches to authentication. We identified a lack of taxonomy for data models that particularly focus on IoHT data architecture to improve the feasibility of AMFA. This viewpoint focuses on identifying key cybersecurity challenges in a theoretical framework for a data model that summarizes the main components of IoHT data. The data are to be used in modalities that are suited for health care users in modern IoHT environments and in response to the COVID-19 pandemic. To establish the data taxonomy, a review of recent IoHT papers was conducted to discuss the related work in IoHT data management and use in next-generation authentication systems. Reports, journal articles, conferences, and white papers were reviewed for IoHT authentication data technologies in relation to the problem statement of remote authentication and user management systems. Only publications written in English from the last decade were included (2012-2022) to identify key issues within the current health care practices and their management of IoHT devices. We discuss the components of the IoHT architecture from the perspective of data management and sensitivity to ensure privacy for all users. The data model addresses the security requirements of IoHT users, environments, and devices toward the automation of AMFA in health care. We found that in health care authentication, the significant threats occurring were related to data breaches owing to weak security options and poor user configuration of IoHT devices. The security requirements of IoHT data architecture and identified impactful methods of cybersecurity for health care devices, data, and their respective attacks are discussed. Data taxonomy provides better understanding, solutions, and improvements of user authentication in remote working environments for security features.

Integrating Artificial Intelligence and Wearable IoT System in Long-Term Care Environments

With the rapid advancement of information and communication technology (ICT), big data, and artificial intelligence (AI), intelligent healthcare systems have emerged, including the integration of healthcare systems with capital, the introduction of healthcare systems into long-term care institutions, and the integration of measurement data for care or exposure. These systems provide comprehensive communication and home exposure reports and enable the involvement of rehabilitation specialists and other experts. Silver technology enables the realization of health management in long-term care services, workplace care, and health applications, facilitating disease prevention and control, improving disease management, reducing home isolation, alleviating family burden in terms of nursing, and promoting health and disease control. Research and development efforts in forward-looking cross-domain precision health technology, system construction, testing, and integration are carried out. This integrated project consists of two main components. The Integrated Intelligent Long-Term Care Service Management System focuses on building a personalized care service system for the elderly, encompassing health, nutrition, diet, and health education aspects. The Wearable Internet of Things Care System primarily supports the development of portable physiological signal detection devices and electronic fences.

Trends in Smart Helmets With Multimodal Sensing for Health and Safety: Scoping Review

BACKGROUND

As a form of the Internet of Things (IoT)-gateways, a smart helmet is one of the core devices that offers distinct functionalities. The development of smart helmets connected to IoT infrastructure helps promote connected health and safety in various fields. In this regard, we present a comprehensive analysis of smart helmet technology and its main characteristics and applications for health and safety.

OBJECTIVE

This paper reviews the trends in smart helmet technology and provides an overview of the current and future potential deployments of such technology, the development of smart helmets for continuous monitoring of the health status of users, and the surrounding environmental conditions. The research questions were as follows: What are the main purposes and domains of smart helmets for health and safety? How have researchers realized key features and with what types of sensors?

METHODS

We selected studies cited in electronic databases such as Google Scholar, Web of Science, ScienceDirect, and EBSCO on smart helmets through a keyword search from January 2010 to December 2021. In total, 1268 papers were identified (Web of Science: 87/1268, 6.86%; EBSCO: 149/1268, 11.75%; ScienceDirect: 248/1268, 19.55%; and Google Scholar: 784/1268, 61.82%), and the number of final studies included after PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) study selection was 57. We also performed a self-assessment of the reviewed articles to determine the quality of the paper. The scoring was based on five criteria: test environment, prototype quality, feasibility test, sensor calibration, and versatility.

RESULTS

Smart helmet research has been considered in industry, sports, first responder, and health tracking scenarios for health and safety purposes. Among 57 studies, most studies with prototype development were industrial applications (18/57, 32%), and the 2 most frequent studies including simulation were industry (23/57, 40%) and sports (23/57, 40%) applications. From our assessment-scoring result, studies tended to focus on sensor calibration results (2.3 out of 3), while the lowest part was a feasibility test (1.6 out of 3). Further classification of the purpose of smart helmets yielded 4 major categories, including activity, physiological and environmental (hazard) risk sensing, as well as risk event alerting.

CONCLUSIONS

A summary of existing smart helmet systems is presented with a review of the sensor features used in the prototyping demonstrations. Overall, we aimed to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart helmets as promising wearable devices. The barriers to users, challenges in the development of smart helmets, and future opportunities for health and safety applications are also discussed. In conclusion, this paper presents the current status of smart helmet technology, main issues, and prospects for future smart helmet with the objective of making the smart helmet concept a reality.

Developing a Multicriteria Decision-Making Model Based on a Three-Layer Virtual Internet of Things Algorithm Model to Rank Players’ Value

This paper proposes a multicriteria decision-making model based on a three-layer virtual internet of things (IoT) algorithm to automatically track and evaluate professional football players’ performance over the Internet. The three layers were respectively related to (1) automated data reading, (2) the players’ comprehensive grey relational degree calculation, and (3) the players’ classification. The methodology was applied in the context of the COVID-19 pandemic to investigate the performance of the top 10 defenders (according to The Sun, an internationally renowned sports website) in the European leagues, participating in the knockout phase of the 2019–20 UEFA Champions League. The results indicate that Virgil van Dijk of Liverpool FC was the best defender, followed by Harry Maguire of Manchester United, and Sergio Ramos of Real Madrid in the second and third positions, respectively. However, this ranking contradicted that of The Sun’s, which ranked these defenders in the seventh, tenth, and eighth positions, respectively. These results can help club management, coaches, and teams negotiate price positioning and future contract renewals or player transfers.

Meta-Context Ontology for Self-Adaptive Mobile Web Service Discovery in Smart Systems

Self-adaptive mobile web service (MWS) discovery in Smart Systems depends on heterogeneous context-sensitive attributes that are composed dynamically to satisfy the user’s MWS request despite the constantly changing environment. Several ontologies are developed to deal with the issues of heterogeneous knowledge representation in smart systems. Unfortunately, these ontologies are mostly inexpressive, unextendible, constricted, and slightly fragmented due to a lack of socially focused ontology development procedures. In this paper, a context ontology for self-adaptive MWS discovery in smart systems is proposed to provide a better representation of the heterogeneous context for smart systems and facilitate the delivery of MWS. The lightweight unified process for ontology building (UPON Lite) is adopted for ontology development. The ontology is experimentally evaluated in protégé using real-world smart systems from the healthcare and agriculture domain. Consequently, the ontology was found to be more expressive, extensible, and support self-adaptive MWS discovery in Smart Systems.

Collision-Based Window-Scaled Back-Off Mechanism for Dense Channel Resource Allocation in Future Wi-Fi

Wireless local area networks (WLANs), known as Wi-Fi, are widely deployed to meet the enhanced needs of data-centric internet applications, such as wireless docking, unified communications, cloud computing, interactive multimedia gaming, progressive streaming, support of wearable devices, up-link broadcasts and cellular offloading. Wi-Fi networks typically adopt the Distributed Coordination Function (DCF)-based Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), which uses the Binary Exponential Back-off (BEB) algorithm at the MAC layer mechanism to access channel resources. Currently deployed Wi-Fi networks face huge challenges towards efficient channel access for denser environments due to the blind exponential increase/decrease of a contention window (CW) procedure that is inefficient for a higher number of contending stations. Several modifications and amendments have been proposed to improve the performance of the MAC layer channel access based on a fixed or variable CW size. However, a more realistic network density-based channel resource allocation solution is still missing. An efficient channel resource allocation is one of the most critical challenges for future highly dense WLANs, such as High-Efficiency WLAN (HEW). In this paper, we propose a Channel Collision-based Window Scaled Back-off (CWSB) mechanism for channel resource allocation in HEW. In our proposed CWSB, all contending stations select an optimized CW size for each back-off stage for collided or successfully transmitted data frames. We affirm the performance of the proposed CWSB mechanism with the help of an Iterative Discrete Time Markov Chain (I-DTMC) model. This paper evaluates the performance of our proposed CWSB mechanism in HEW Wi-Fi networks using an NS3 simulator in terms of the normalized throughput and channel access delay compared to the state-of-the-art BEB and a recently proposed mechanism.

Is Silence Golden? Chronic Stress and Psychophysiological Indicators’ Changes over Time in International Students: A Pilot Study

Most people assume studying or working abroad would be stressful - but would one ever think that it could be detrimental to the health? Stress literature relates cross-cultural transactions to the chances of gaining higher levels of chronic stress. This paper reports the results of two studies on international students in Moscow in 2018. Specifically, Study 1 assessed how cross-cultural transactions perceived to affect health state during the first 6 months of their relocation. Study 2 aimed to investigate if the psychological stress linked to relocation to a different country can possibly lead to psychobiological effects of chronic stress. In Study 1, qualitative methods were applied to conduct 21 interviews with international students. In Study 2, a longitudinal pilot study was conducted for 10 foreign students during the first 2-5 (M = 3.6) months of their relocation. Stress related to cross-cultural transactions was expected to affect subjective well-being and health variables. The health state was a relatively silent topic in the interview participants of Study 1. The results of Study 2 showed that the participants had changes in the resting heart rate (RHR) baseline. Perceived chronic stress related to cross-cultural transactions may affect psychophysiological state; however, the affect varies depending on a person. Further research is required for the data consistency and for identifying non-invasive objective risk markers and individual stress pathways, with the goal of identifying at-risk students and providing treatment options before any serious harm is done to their health.

Smart Clothing Framework for Health Monitoring Applications

Wearable technologies are making a significant impact on people’s way of living thanks to the advancements in mobile communication, internet of things (IoT), big data and artificial intelligence. Conventional wearable technologies present many challenges for the continuous monitoring of human health conditions due to their lack of flexibility and bulkiness in size. Recent development in e-textiles and the smart integration of miniature electronic devices into textiles have led to the emergence of smart clothing systems for remote health monitoring. A novel comprehensive framework of smart clothing systems for health monitoring is proposed in this paper. This framework provides design specifications, suitable sensors and textile materials for smart clothing (e.g., leggings) development. In addition, the proposed framework identifies techniques for empowering the seamless integration of sensors into textiles and suggests a development strategy for health diagnosis and prognosis through data collection, data processing and decision making. The conceptual technical specification of smart clothing is also formulated and presented. The detailed development of this framework is presented in this paper with selected examples. The key challenges in popularizing smart clothing and opportunities of future development in diverse application areas such as healthcare, sports and athletics and fashion are discussed.

Biomedical IoT: Enabling Technologies, Architectural Elements, Challenges, and Future Directions

This paper provides a comprehensive literature review of various technologies and protocols used for medical Internet of Things (IoT) with a thorough examination of current enabling technologies, use cases, applications, and challenges. Despite recent advances, medical IoT is still not considered a routine practice. Due to regulation, ethical, and technological challenges of biomedical hardware, the growth of medical IoT is inhibited. Medical IoT continues to advance in terms of biomedical hardware, and monitoring figures like vital signs, temperature, electrical signals, oxygen levels, cancer indicators, glucose levels, and other bodily levels. In the upcoming years, medical IoT is expected replace old healthcare systems. In comparison to other survey papers on this topic, our paper provides a thorough summary of the most relevant protocols and technologies specifically for medical IoT as well as the challenges. Our paper also contains several proposed frameworks and use cases of medical IoT in hospital settings as well as a comprehensive overview of previous architectures of IoT regarding the strengths and weaknesses. We hope to enable researchers of multiple disciplines, developers, and biomedical engineers to quickly become knowledgeable on how various technologies cooperate and how current frameworks can be modified for new use cases, thus inspiring more growth in medical IoT.

Remote health monitoring system using heterogeneous networks

This paper presents the implementation of a remote health monitoring system by using Heterogeneous Networks (HetNet), in which remote patients' vital data can be sent to the proximate hospital with very low end‐to‐end latency. To carry out the aforementioned process, patients' statistics are delivered initially from Wireless Body Area Network (WBAN) to the patients' mobile phone by using ISM band. Then, from there, contemporary networks make use of single wireless network alone to send the patients' data to the nearest hospital (even though there are multiple networks in a terrain). But, this particular network may have so much of end‐to‐end latency as a consequence of lack of resources in the network. However, in the proposed work, all the available heterogeneous Radio Access Technology (RAT) networks carry multiple patients' statistics to the nearest hospital by using either the RAT's free channels (in licensed band) or white space channels. Further, in order to reduce the latency in the proposed system, a novel hand‐off method is suggested in this paper by exploiting SDR features. Moreover, simulation results reveal the effectiveness of the proposed system in terms of end‐to‐end latency and spectral efficiency.

NextGen Public Health Surveillance and the Internet of Things (IoT)

Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.

Mobile 5P-Medicine Approach for Cardiovascular Patients

Medicine is heading towards personalized care based on individual situations and conditions. With smartphones and increasingly miniaturized wearable devices, the sensors available on these devices can perform long-term continuous monitoring of several user health-related parameters, making them a powerful tool for a new medicine approach for these patients. Our proposed system, described in this article, aims to develop innovative solutions based on artificial intelligence techniques to empower patients with cardiovascular disease. These solutions will realize a novel 5P (Predictive, Preventive, Participatory, Personalized, and Precision) medicine approach by providing patients with personalized plans for treatment and increasing their ability for self-monitoring. Such capabilities will be derived by learning algorithms from physiological data and behavioral information, collected using wearables and smart devices worn by patients with health conditions. Further, developing an innovative system of smart algorithms will also focus on providing monitoring techniques, predicting extreme events, generating alarms with varying health parameters, and offering opportunities to maintain active engagement of patients in the healthcare process by promoting the adoption of healthy behaviors and well-being outcomes. The multiple features of this future system will increase the quality of life for cardiovascular diseases patients and provide seamless contact with a healthcare professional.

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications.

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

IoT has played an essential role in many industries over the last few decades. Recent advancements in the healthcare industry have made it possible to make healthcare accessible to more people and improve their overall health. The next step in healthcare is to integrate it with IoT-assisted wearable sensor systems seamlessly. This review rigorously discusses the various IoT architectures, different methods of data processing, transfer, and computing paradigms. It compiles various communication technologies and the devices commonly used in IoT-assisted wearable sensor systems and deals with its various applications in healthcare and their advantages to the world. A comparative analysis of all the wearable technology in healthcare is also discussed with tabulation of various research and technology. This review also analyses all the problems commonly faced in IoT-assisted wearable sensor systems and the specific issues that need to be tackled to optimize these systems in healthcare and describes the various future implementations that can be made to the architecture and the technology to improve the healthcare industry.

A scalable, secure, and interoperable platform for deep data-driven health management

The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.

OFDMA Backoff Control Scheme for Improving Channel Efficiency in the Dynamic Network Environment of IEEE 802.11ax WLANs

IEEE 802.11ax uplink orthogonal frequency division multiple access (OFDMA)-based random access (UORA) is a new feature for random channel access in wireless local area networks (WLANs). Similar to the legacy random access scheme in WLANs, UORA performs the OFDMA backoff (OBO) procedure to access the channel and decides on a random OBO counter within the OFDMA contention window (OCW) value. An access point (AP) can determine the OCW range and inform each station (STA) of it. However, how to determine a reasonable OCW range is beyond the scope of the IEEE 802.11ax standard. The OCW range is crucial to the UORA performance, and it primarily depends on the number of contending STAs, but it is challenging for the AP to accurately and quickly estimate or keep track of the number of contending STAs without the aid of a specific signaling mechanism. In addition, the one for this purpose incurs an additional delay and overhead in the channel access procedure. Therefore, the performance of a UORA scheme can be degraded by an improper OCW range, especially when the number of contending STAs changes dynamically. We first observed the effect of OCW values on channel efficiency and derived its optimal value from an analytical model. Next, we proposed a simple yet effective OBO control scheme where each STA determines its own OBO counter in a distributed manner rather than adjusting the OCW value globally. In the proposed scheme, each STA determines an appropriate OBO counter depending on whether the previous transmission was successful or not so that collisions can be mitigated without leaving OFDMA resource units unnecessarily idle. The results of a simulation study confirm that the throughput of the proposed scheme is comparable to the optimal OCW-based scheme and is improved by up to 15 times compared to the standard UORA scheme.

Smart Manufacturing Real-Time Analysis Based on Blockchain and Machine Learning Approaches

The growth of data production in the manufacturing industry causes the monitoring system to become an essential concept for decision-making and management. The recent powerful technologies, such as the Internet of Things (IoT), which is sensor-based, can process suitable ways to monitor the manufacturing process. The proposed system in this research is the integration of IoT, Machine Learning (ML), and for monitoring the manufacturing system. The environmental data are collected from IoT sensors, including temperature, humidity, gyroscope, and accelerometer. The data types generated from sensors are unstructured, massive, and real-time. Various big data techniques are applied to further process of the data. The hybrid prediction model used in this system uses the Random Forest classification technique to remove the sensor data outliers and donate fault detection through the manufacturing system. The proposed system was evaluated for automotive manufacturing in South Korea. The technique applied in this system is used to secure and improve the data trust to avoid real data changes with fake data and system transactions. The results section provides the effectiveness of the proposed system compared to other approaches. Moreover, the hybrid prediction model provides an acceptable fault prediction than other inputs. The expected process from the proposed method is to enhance decision-making and reduce the faults through the manufacturing process.

IoT-Based Applications in Healthcare Devices

The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things (IoT) has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up-to-date summary of the potential healthcare applications of IoT- (HIoT-) based technologies. Herein, the advancement of the application of the HIoT has been reported from the perspective of enabling technologies, healthcare services, and applications in solving various healthcare issues. Moreover, potential challenges and issues in the HIoT system are also discussed. In sum, the current study provides a comprehensive source of information regarding the different fields of application of HIoT intending to help future researchers, who have the interest to work and make advancements in the field to gain insight into the topic.

Internet of medical things (IoMT)-integrated biosensors for point-of-care testing of infectious diseases

On global scale, the current situation of pandemic is symptomatic of increased incidences of contagious diseases caused by pathogens. The faster spread of these diseases, in a moderately short timeframe, is threatening the overall population wellbeing and conceivably the economy. The inadequacy of conventional diagnostic tools in terms of time consuming and complex laboratory-based diagnosis process is a major challenge to medical care. In present era, the development of point-of-care testing (POCT) is in demand for fast detection of infectious diseases along with “on-site” results that are helpful in timely and early action for better treatment. In addition, POCT devices also play a crucial role in preventing the transmission of infectious diseases by offering real-time testing and lab quality microbial diagnosis within minutes. Timely diagnosis and further treatment optimization facilitate the containment of outbreaks of infectious diseases. Presently, efforts are being made to support such POCT by the technological development in the field of internet of medical things (IoMT). The IoMT offers wireless-based operation and connectivity of POCT devices with health expert and medical centre. In this review, the recently developed POC diagnostics integrated or future possibilities of integration with IoMT are discussed with focus on emerging and re-emerging infectious diseases like malaria, dengue fever, influenza A (H1N1), human papilloma virus (HPV), Ebola virus disease (EVD), Zika virus (ZIKV), and coronavirus (COVID-19). The IoMT-assisted POCT systems are capable enough to fill the gap between bioinformatics generation, big rapid analytics, and clinical validation. An optimized IoMT-assisted POCT will be useful in understanding the diseases progression, treatment decision, and evaluation of efficacy of prescribed therapy.

Cloud based ensemble machine learning approach for smart detection of epileptic seizures using higher order spectral analysis

The present paper proposes a smart framework for detection of epileptic seizures using the concepts of IoT technologies, cloud computing and machine learning. This framework processes the acquired scalp EEG signals by Fast Walsh Hadamard transform. Then, the transformed frequency-domain signals are examined using higher-order spectral analysis to extract amplitude and entropy-based statistical features. The extracted features have been selected by means of correlation-based feature selection algorithm to achieve more real-time classification with reduced complexity and delay. Finally, the samples containing selected features have been fed to ensemble machine learning techniques for classification into several classes of EEG states, viz. normal, interictal and ictal. The employed techniques include Dagging, Bagging, Stacking, MultiBoost AB and AdaBoost M1 algorithms in integration with C4.5 decision tree algorithm as the base classifier. The results of the ensemble techniques are also compared with standalone C4.5 decision tree and SVM algorithms. The performance analysis through simulation results reveals that the ensemble of AdaBoost M1 and C4.5 decision tree algorithms with higher-order spectral features is an adequate technique for automated detection of epileptic seizures in real-time. This technique achieves 100% classification accuracy, sensitivity and specificity values with optimally small classification time.

Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients

Schizophrenia is a fatal mental disorder, which affects millions of people globally by the disturbance in their thinking, feeling and behaviour. In the age of the internet of things assisted with cloud computing and machine learning techniques, the computer-aided diagnosis of schizophrenia is essentially required to provide its patients with an opportunity to own a better quality of life. In this context, the present paper proposes a spectral features based convolutional neural network (CNN) model for accurate identification of schizophrenic patients using spectral analysis of multichannel EEG signals in real-time. This model processes acquired EEG signals with filtering, segmentation and conversion into frequency domain. Then, given frequency domain segments are divided into six distinct spectral bands like delta, theta-1, theta-2, alpha, beta and gamma. The spectral features including mean spectral amplitude, spectral power and Hjorth descriptors (Activity, Mobility and Complexity) are extracted from each band. These features are independently fed to the proposed spectral features-based CNN and long short-term memory network (LSTM) models for classification. This work also makes use of raw time-domain and frequency-domain EEG segments for classification using temporal CNN and spectral CNN models of same architectures respectively. The overall analysis of simulation results of all models exhibits that the proposed spectral features based CNN model is an efficient technique for accurate and prompt identification of schizophrenic patients among healthy individuals with average classification accuracies of 94.08% and 98.56% for two different datasets with optimally small classification time.

Use of social media big data as a novel HIV surveillance tool in South Africa

Sub-Saharan Africa has been heavily impacted by the HIV/AIDS epidemic. Social data (e.g., social media, internet search, wearable device, etc) show great promise assisting in public health and HIV surveillance. However, research on this topic has primarily focused in higher resource settings, such as the United States. It is especially important to study the prevalence and potential use of these data sources and tools in low- and middle-income countries (LMIC), such as Sub-Saharan Africa, which have been heavily impacted by the HIV epidemic, to determine the feasibility of using these technologies as surveillance and intervention tools. Accordingly, we 1) described the prevalence and characteristics of various social technologies within South Africa, 2) using Twitter, Instagram, and YouTube as a case study, analyzed the prevalence and patterns of social media use related to HIV risk in South Africa, and 3) mapped and statistically tested differences in HIV-related social media posts within regions of South Africa. Geocoded data were collected over a three-week period in 2018 (654,373 tweets, 90,410 Instagram posts and 14,133 YouTube videos with 1,121 comments). Of all tweets, 4,524 (0.7%) were found to related to HIV and AIDS. The percentage was similar for Instagram 95 (0.7%) but significantly lower for YouTube 18 (0.1%). We found regional differences in prevalence and use of social media related to HIV. We discuss the implication of data from these technologies in surveillance and interventions within South Africa and other LMICs.

A Wristwatch-Based Wireless Sensor Platform for IoT Health Monitoring Applications

A wristwatch-based wireless sensor platform for IoT wearable health monitoring applications is presented. The paper describes the platform in detail, with a particular focus given to the design of a novel and compact wireless sub-system for 868 MHz wristwatch applications. An example application using the developed platform is discussed for arterial oxygen saturation (SpO₂) and heart rate measurement using optical photoplethysmography (PPG). A comparison of the wireless performance in the 868 MHz and the 2.45 GHz bands is performed. Another contribution of this work is the development of a highly integrated 868 MHz antenna. The antenna structure is printed on the surface of a wristwatch enclosure using laser direct structuring (LDS) technology. At 868 MHz, a low specific absorption rate (SAR) of less than 0.1% of the maximum permissible limit in the simulation is demonstrated. The measured on-body prototype antenna exhibits a −10 dB impedance bandwidth of 36 MHz, a peak realized gain of −4.86 dBi and a radiation efficiency of 14.53% at 868 MHz. To evaluate the performance of the developed 868 MHz sensor platform, the wireless communication range measurements are performed in an indoor environment and compared with a commercial Bluetooth wristwatch device.

Issues and challenges of user and data interaction in healthcare-related IoT

Purpose

The Internet of Things (IoT), which enables smart objects to collect and exchange data, has a variety of application domains used in everyday life including healthcare. As a set of promising next-generation technologies in the healthcare domain, Healthcare-related Internet of Things (H-IoT) promises to facilitate better healthcare by offering data-driven insights. While effective in practice at large, emerging data concerns arise because of the inscrutable black-box systems. Inspired by the notion of human data interaction, this paper seeks to understand how people engage with the H-IoT data that is about and produced by themselves and to elucidate the main data issues and challenges involved in the development of H-IoT.

Design/methodology/approach

This work conducted a comprehensive survey and integrated the method of content analysis by systematically review the recently published H-IoT research work in the healthcare domain.

Findings

This study thoroughly surveyed more than 300 research studies published in the last decades and classified seven H-IoT end-user groups, and three H-IoT data types that are important to H-IoT comprehension. Attention to human data interaction, our study also highlights several critical issues associated with this notion in the context of H-IoT.

Originality/value

This study will support H-IoT research by characterizing the data issues and challenges exist in the context of H-IoT user and data interaction. The findings will provide insights in designing for effective interactions with data in the H-IoT.

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

Recently, there has been a cloud-based Internet of Medical Things (IoMT) solution offering different healthcare services to wearable sensor devices for patients. These services are global, and can be invoked anywhere at any place. Especially, electrocardiogram (ECG) sensors, such as Lead I and Lead II, demands continuous cloud services for real-time execution. However, these services are paid and need a lower cost-efficient process for the users. In this paper, this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system. The objective was to offer omnipresent cloud services to the generated data with minimum cost. This study proposed a novel health care based fog cloud system (HCBFS) to collect, analyze, and determine the process of critical tasks of the heartbeat medical application for the purpose of minimizing the total cost. This study devised a health care awareness cost-efficient task scheduling (HCCETS) algorithm framework, which not only schedule all tasks with minimum cost, but also executes them on their deadlines. Performance evaluation shows that the proposed task scheduling algorithm framework outperformed the existing algorithm methods in terms of cost.

Internet of Things, Digital Biomarker, and Artificial Intelligence in Spine: Current and Future Perspectives

Recent interest in medical artificial intelligence (AI) has increased with onset of the fourth industrial revolution. Real-time monitoring of patients is an important research area of medical AI. The medical AI is very closely related to the Internet of Things (IoT), a core element of the fourth industrial revolution. Attempts to diagnose and treat patients using IoT have been already applied to patients with chronic disease such as hypertension and arrhythmia. However, in the spine, research on IoT and digital biomarkers are still in the early stages. The digital biomarker obtained by IoT devices is objective and could represent real-time, real-world, and abundant data. Based on its characteristics, IoT and digital biomarkers can also be useful in the spine. Currently, research on real-time monitoring of physical activity or spinal posture is ongoing. Therefore, the authors introduce the basic concepts of IoT and digital biomarkers, their relationship to AI, and recent trends. Current and future perspectives of IoT and digital biomarker in spine are also discussed. In the future, it is expected that IoT, digital biomarkers, and AI will lead to a paradigm shift in the diagnosis and treatment of spinal diseases.

A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification

BACKGROUND AND OBJECTIVE

Electrocardiogram (ECG) is a useful tool for detecting heart disease. Automated ECG diagnosis allows for heart monitoring on small devices, especially on wearable devices. In order to recognize arrhythmias automatically, accurate classification method for electrocardiogram (ECG) heartbeats was studied in this paper.

METHODS

Based on weighted extreme gradient boosting (XGBoost), a hierarchical classification method is proposed. A large number of features from 6 categories are extracted from the preprocessed heartbeats. Then recursive feature elimination is used for selecting features. Afterwards, a hierarchical classifier is constructed in classification stage. The hierarchical classifier is composed of threshold and XGBoost classifiers. And the XGBoost classifiers are improved with weights.

RESULTS

The method was applied to an inter-patient experiment conforming AAMI standard. The obtained sensitivities for normal (N), supraventricular (S), ventricular (V), fusion (F), and Unknown beats (Q) were 92.1%, 91.7%, 95.1%, and 61.6%. Positive predictive values of 99.5%, 46.2%, 88.1%, and 15.2% were also provided for the four classes.

CONCLUSIONS

XGBoost was improved and firstly introduced in single heartbeat classification. A comparison showed the effectiveness of the novel method. The method was more suitable for clinical application as both high positive predictive value for N class and high sensitivities for abnormal classes were provided.

A Simple Secret Key Generation by Using a Combination of Pre-Processing Method with a Multilevel Quantization

Limitations of the computational and energy capabilities of IoT devices provide new challenges in securing communication between devices. Physical layer security (PHYSEC) is one of the solutions that can be used to solve the communication security challenges. In this paper, we conducted an investigation on PHYSEC which utilizes channel reciprocity in generating a secret key, commonly known as secret key generation (SKG) schemes. Our research focused on the efforts to get a simple SKG scheme by eliminating the information reconciliation stage so as to reduce the high computational and communication cost. We exploited the pre-processing method by proposing a modified Kalman (MK) and performing a combination of the method with a multilevel quantization, i.e., combined multilevel quantization (CMQ). Our approach produces a simple SKG scheme for its significant increase in reciprocity so that an identical secret key between two legitimate users can be obtained without going through the information reconciliation stage.

Secure Smart Cameras by Aggregate-Signcryption with Decryption Fairness for Multi-Receiver IoT Applications

Smart cameras are key sensors in Internet of Things (IoT) applications and often capture highly sensitive information. Therefore, security and privacy protection is a key concern. This paper introduces a lightweight security approach for smart camera IoT applications based on elliptic-curve (EC) signcryption that performs data signing and encryption in a single step. We deploy signcryption to efficiently protect sensitive data onboard the cameras and secure the data transfer from multiple cameras to multiple monitoring devices. Our multi-sender/multi-receiver approach provides integrity, authenticity, and confidentiality of data with decryption fairness for multiple receivers throughout the entire lifetime of the data. It further provides public verifiability and forward secrecy of data. Our certificateless multi-receiver aggregate-signcryption protection has been implemented for a smart camera IoT scenario, and the runtime and communication effort has been compared with single-sender/single-receiver and multi-sender/single-receiver setups.

Moving to the Edge-Cloud-of-Things: Recent Advances and Future Research Directions

Cloud computing has significantly enhanced the growth of the Internet of Things (IoT) by ensuring and supporting the Quality of Service (QoS) of IoT applications. However, cloud services are still far from IoT devices. Notably, the transmission of IoT data experiences network issues, such as high latency. In this case, the cloud platforms cannot satisfy the IoT applications that require real-time response. Yet, the location of cloud services is one of the challenges encountered in the evolution of the IoT paradigm. Recently, edge cloud computing has been proposed to bring cloud services closer to the IoT end-users, becoming a promising paradigm whose pitfalls and challenges are not yet well understood. This paper aims at presenting the leading-edge computing concerning the movement of services from centralized cloud platforms to decentralized platforms, and examines the issues and challenges introduced by these highly distributed environments, to support engineers and researchers who might benefit from this transition.

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

Clutch Pedal Sensorization and Evaluation of the Main Parameters Related to Driver Posture

An improper decision for the design, selection and adjustment of the components needed to control a vehicle could generate negative effects and discomfort to the driver, where pedals play a very important role. The aim of the study is to provide a first approach to develop an embedded monitoring device in order to evaluate the posture of the driver, the influence of the clutch pedal and to advise about the possible risk. With that purpose in mind, a testbed was designed and two different sets of tests were carried out. The first test collected information about the volunteers who were part of the experiment, like the applied force on the clutch pedal or the body measurements. The second test was carried out to provide new insight into this matter. One of the more significant findings to emerge from this study is that the force applied on the clutch pedal provides enough information to determine correct driver posture. For this reason, a system composed of a pedal force sensor and an acquisition/processing system can fulfil the requirements to create a healthcare system focused on driver posture.

A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing

Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users’ vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning–based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.

Collaborative Working Architecture for IoT-Based Applications

The new sensing applications need enhanced computing capabilities to handle the requirements of complex and huge data processing. The Internet of Things (IoT) concept brings processing and communication features to devices. In addition, the Cloud Computing paradigm provides resources and infrastructures for performing the computations and outsourcing the work from the IoT devices. This scenario opens new opportunities for designing advanced IoT-based applications, however, there is still much research to be done to properly gear all the systems for working together. This work proposes a collaborative model and an architecture to take advantage of the available computing resources. The resulting architecture involves a novel network design with different levels which combines sensing and processing capabilities based on the Mobile Cloud Computing (MCC) paradigm. An experiment is included to demonstrate that this approach can be used in diverse real applications. The results show the flexibility of the architecture to perform complex computational tasks of advanced applications.

Self-Organizing Peer-To-Peer Middleware for Healthcare Monitoring in Real-Time

As the number of elderly persons with chronic illnesses increases, a new public infrastructure for their care is becoming increasingly necessary. In particular, technologies that can monitoring bio-signals in real-time have been receiving significant attention. Currently, most healthcare monitoring services are implemented by wireless carrier through centralized servers. These services are vulnerable to data concentration because all data are sent to a remote server. To solve these problems, we propose self-organizing P2P middleware for healthcare monitoring that enables a real-time multi bio-signal streaming without any central server by connecting the caregiver and care recipient. To verify the performance of the proposed middleware, we evaluated the monitoring service matching time based on a monitoring request. We also confirmed that it is possible to provide an effective monitoring service by evaluating the connectivity between Peer-to-Peer and average jitter.