Exercise Promotion System for Single Households Based on Agent-Oriented IoT Architecture.

People living alone encounter well-being challenges due to unnoticed personal situations. Thus, it is essential to monitor their activities and encourage them to adopt healthy lifestyle habits without imposing a mental burden, aiming to enhance their overall well-being. To realize such a support system, its components should be simple and loosely coupled to handle various internet of things (IoT)-based smart home applications. In this study, we propose an exercise promotion system for individuals living alone to encourage them to adopt good lifestyle habits. The system comprises autonomous IoT devices as agents and is realized using an agent-oriented IoT architecture. It estimates user activity via sensors and offers exercise advice based on recognized conditions, surroundings, and preferences. The proposed system accepts user feedback to improve status estimation accuracy and offers better advice. The proposed system was evaluated from three perspectives through experiments with subjects. Initially, we demonstrated the system's operation through agent cooperation. Then, we showed it adapts to user preferences within two weeks. Third, the users expressed satisfaction with the detection accuracy regarding their stay-at-home status and the relevance of the advice provided. They were also motivated to engage in exercise based on a subjective evaluation, as indicated by preliminary results.

DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents.

Traffic accidents present significant risks to human life, leading to a high number of fatalities and injuries. According to the World Health Organization's 2022 worldwide status report on road safety, there were 27,582 deaths linked to traffic-related events, including 4448 fatalities at the collision scenes. Drunk driving is one of the leading causes contributing to the rising count of deadly accidents. Current methods to assess driver alcohol consumption are vulnerable to network risks, such as data corruption, identity theft, and man-in-the-middle attacks. In addition, these systems are subject to security restrictions that have been largely overlooked in earlier research focused on driver information. This study intends to develop a platform that combines the Internet of Things (IoT) with blockchain technology in order to address these concerns and improve the security of user data. In this work, we present a device- and blockchain-based dashboard solution for a centralized police monitoring account. The equipment is responsible for determining the driver's impairment level by monitoring the driver's blood alcohol concentration (BAC) and the stability of the vehicle. At predetermined times, integrated blockchain transactions are executed, transmitting data straight to the central police account. This eliminates the need for a central server, ensuring the immutability of data and the existence of blockchain transactions that are independent of any central authority. Our system delivers scalability, compatibility, and faster execution times by adopting this approach. Through comparative research, we have identified a significant increase in the need for security measures in relevant scenarios, highlighting the importance of our suggested model.

Internet of Things in Pregnancy Care Coordination and Management: A Systematic Review.

The Internet of Things (IoT) has positioned itself globally as a dominant force in the technology sector. IoT, a technology based on interconnected devices, has found applications in various research areas, including healthcare. Embedded devices and wearable technologies powered by IoT have been shown to be effective in patient monitoring and management systems, with a particular focus on pregnant women. This study provides a comprehensive systematic review of the literature on IoT architectures, systems, models and devices used to monitor and manage complications during pregnancy, postpartum and neonatal care. The study identifies emerging research trends and highlights existing research challenges and gaps, offering insights to improve the well-being of pregnant women at a critical moment in their lives. The literature review and discussions presented here serve as valuable resources for stakeholders in this field and pave the way for new and effective paradigms. Additionally, we outline a future research scope discussion for the benefit of researchers and healthcare professionals.

IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic.

A global health emergency resulted from the COVID-19 epidemic. Image recognition techniques are a useful tool for limiting the spread of the pandemic; indeed, the World Health Organization (WHO) recommends the use of face masks in public places as a form of protection against contagion. Hence, innovative systems and algorithms were deployed to rapidly screen a large number of people with faces covered by masks. In this article, we analyze the current state of research and future directions in algorithms and systems for masked-face recognition. First, the paper discusses the importance and applications of facial and face mask recognition, introducing the main approaches. Afterward, we review the recent facial recognition frameworks and systems based on Convolution Neural Networks, deep learning, machine learning, and MobilNet techniques. In detail, we analyze and critically discuss recent scientific works and systems which employ machine learning (ML) and deep learning tools for promptly recognizing masked faces. Also, Internet of Things (IoT)-based sensors, implementing ML and DL algorithms, were described to keep track of the number of persons donning face masks and notify the proper authorities. Afterward, the main challenges and open issues that should be solved in future studies and systems are discussed. Finally, comparative analysis and discussion are reported, providing useful insights for outlining the next generation of face recognition systems.

An Advanced Internet of Things System for Heatstroke Prevention with a Noninvasive Dual-Heat-Flux Thermometer.

Heatstroke is a concern during sudden heat waves. We designed and prototyped an Internet of Things system for heatstroke prevention, which integrates physiological information, including deep body temperature (DBT), based on the dual-heat-flux method. A dual-heat-flux thermometer developed to monitor DBT in real-time was also evaluated. Real-time readings from the thermometer are stored on a cloud platform and processed by a decision rule, which can alert the user to heatstroke. Although the validation of the system is ongoing, its feasibility is demonstrated in a preliminary experiment.

Real-Time Learning and Monitoring System in Fighting against SARS-CoV-2 in a Private Indoor Environment.

The SARS-CoV-2 virus has posed formidable challenges that must be tackled through scientific and technological investigations on each environmental scale. This research aims to learn and report about the current state of user activities, in real-time, in a specially designed private indoor environment with sensors in infection transmission control of SARS-CoV-2. Thus, a real-time learning system that evolves and updates with each incoming piece of data from the environment is developed to predict user activities categorized for remote monitoring. Accordingly, various experiments are conducted in the private indoor space. Multiple sensors, with their inputs, are analyzed through the experiments. The experiment environment, installed with microgrids and Internet of Things (IoT) devices, has provided correlating data of various sensors from that special care context during the pandemic. The data is applied to classify user activities and develop a real-time learning and monitoring system to predict the IoT data. The microgrids were operated with the real-time learning system developed by comprehensive experiments on classification learning, regression learning, Error-Correcting Output Codes (ECOC), and deep learning models. With the help of machine learning experiments, data optimization, and the multilayered-tandem organization of the developed neural networks, the efficiency of this real-time monitoring system increases in learning the activity of users and predicting their actions, which are reported as feedback on the monitoring interfaces. The developed learning system predicts the real-time IoT data, accurately, in less than 5 milliseconds and generates big data that can be deployed for different usages in larger-scale facilities, networks, and e-health services.

A Solution for the Remote Care of Frail Elderly Individuals via Exergames.

Internet of Things (IoT) solutions are a concrete answer to many needs in the healthcare framework since they enable remote support for patients and foster continuity of care. Currently, frail elderly people are among end users who most need and would benefit from IoT solutions from both a social and a healthcare point of view. Indeed, IoT technologies can provide a set of services to monitor the healthcare of the elderly or support them in order to reduce the risk of injuries, and preserve their motor and cognitive abilities. The main feature of IoT solutions for the elderly population is ease of use. Indeed, to fully exploit the potential of an IoT solution, patients should be able to autonomously deal with it. The remote-monitoring validation engineering system (ReMoVES) described here is an IoT solution that caters to the specific needs of frail elderly individuals. Its architecture was designed for use at rehabilitation centers and at patients' homes. The system is user-friendly and comfortably usable by persons who are not familiar with technology. In addition, exergames enhance patient engagement in order to curb therapy abandonment. Along with the technical presentation of the solution, a real-life scenario application is described referring to sit-to-stand activity.

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles.

BACKGROUND: The growth of the number of vehicles in traffic has led to an exponential increase in the number of road accidents with many negative consequences, such as loss of lives and pollution. METHODS: This article focuses on using a new technology in automotive electronics by equipping a semi-autonomous vehicle with a complex sensor structure that is able to provide centralized information regarding the physiological signals (Electro encephalogram-EEG, electrocardiogram-ECG) of the driver/passengers and their location along with indoor temperature changes, employing the Internet of Things (IoT) technology. Thus, transforming the vehicle into a mobile sensor connected to the internet will help highlight and create a new perspective on the cognitive and physiological conditions of passengers, which is useful for specific applications, such as health management and a more effective intervention in case of road accidents. These sensor structures mounted in vehicles will allow for a higher detection rate of potential dangers in real time. The approach uses detection, recording, and transmission of relevant health information in the event of an incident as support for e-Call or other emergency services, including telemedicine. RESULTS: The novelty of the research is based on the design of specialized non-invasive sensors for the acquisition of EEG and ECG signals installed in the headrest and backrest of car seats, on the algorithms used for data analysis and fusion, but also on the implementation of an IoT temperature measurement system in several points that simultaneously uses sensors based on MEMS technology. The solution can also be integrated with an e-Call system for telemedicine emergency assistance. CONCLUSION: The research presents both positive and negative results of field experiments, with possible further developments. In this context, the solution has been developed based on state-of-the-art technical devices, methods, and technologies for monitoring vital functions of the driver/passengers (degree of fatigue, cognitive state, heart rate, blood pressure). The purpose is to reduce the risk of accidents for semi-autonomous vehicles and to also monitor the condition of passengers in the case of autonomous vehicles for providing first aid in a timely manner. Reported abnormal values of vital parameters (critical situations) will allow interveneing in a timely manner, saving the patient's life, with the support of the e-Call system.

Internet of Things Smart Sunscreen Station: Descriptive Proof-of-Concept Study.

BACKGROUND: Skin cancer is the most prevalent but also most preventable cancer in Australia. Outdoor workers are at increased risk of developing skin cancer, and improvements in sun protection are needed. Sunscreen, when applied at the recommended concentration (2 mg/cm2), has been shown to block the harmful molecular effects of ultraviolet radiation in vivo. However, sunscreen is often not applied, reapplied sufficiently, or stored adequately to yield protection and reduce sunburns. OBJECTIVE: The primary aim of this study was to test an Internet of Things approach by deploying a smart sunscreen station to an outdoor regional mining site. METHODS: We deployed a smart sunscreen station and examined the key technological considerations including connectivity, security, and data management systems. RESULTS: The smart sunscreen station was deployed for 12 days at a mining workplace (Dalby, Australia). The smart sunscreen station's electrical components remained operational during field testing, and data were received by the message queuing telemetry transport server automatically at the end of each day of field testing (12/12 days, 100% connectivity). CONCLUSIONS: This study highlights that an Internet of Things technology approach can successfully measure sunscreen usage and temperature storage conditions.

The Internet of Things: Impact and Implications for Health Care Delivery.

The Internet of Things (IoT) is a system of wireless, interrelated, and connected digital devices that can collect, send, and store data over a network without requiring human-to-human or human-to-computer interaction. The IoT promises many benefits to streamlining and enhancing health care delivery to proactively predict health issues and diagnose, treat, and monitor patients both in and out of the hospital. Worldwide, government leaders and decision makers are implementing policies to deliver health care services using technology and more so in response to the novel COVID-19 pandemic. It is now becoming increasingly important to understand how established and emerging IoT technologies can support health systems to deliver safe and effective care. The aim of this viewpoint paper is to provide an overview of the current IoT technology in health care, outline how IoT devices are improving health service delivery, and outline how IoT technology can affect and disrupt global health care in the next decade. The potential of IoT-based health care is expanded upon to theorize how IoT can improve the accessibility of preventative public health services and transition our current secondary and tertiary health care to be a more proactive, continuous, and coordinated system. Finally, this paper will deal with the potential issues that IoT-based health care generates, barriers to market adoption from health care professionals and patients alike, confidence and acceptability, privacy and security, interoperability, standardization and remuneration, data storage, and control and ownership. Corresponding enablers of IoT in current health care will rely on policy support, cybersecurity-focused guidelines, careful strategic planning, and transparent policies within health care organizations. IoT-based health care has great potential to improve the efficiency of the health system and improve population health.

The global experience of digital health interventions in COVID-19 management.

Digital health interventions are globally playing a significant role to combat coronavirus disease 2019 (COVID-19), which is an infectious disease caused by Severe Acute Respiratory Syndrome coronavirus 2. Here, we present a very brief overview of the multifaceted digital interventions, globally, and in India, for maintaining health and health-care delivery, in the context of the Covid-19 pandemic.

Pathways to democratized healthcare: Envisioning human-centered AI-as-a-service for customized diagnosis and rehabilitation.

The ongoing digital revolution in the healthcare sector, emphasized by bodies like the US Food and Drug Administration (FDA), is paving the way for a shift towards person-centric healthcare models. These models consider individual needs, turning patients from passive recipients to active participants. A key factor in this shift is Artificial Intelligence (AI), which has the capacity to revolutionize healthcare delivery due to its ability to personalize it. With the rise of software in healthcare and the proliferation of the Internet of Things (IoT), a surge of digital data is being produced. This data, alongside improvements in AI's explainability, is facilitating the spread of person-centric healthcare models, aiming at improving health management and patient experience. This paper outlines a human-centered methodology for the development of an AI-as-a-service platform with the goal of broadening access to personalized healthcare. This approach places humans at its core, aiming to augment, not replace, human capabilities and integrate in current processes. The primary research question guiding this study is: "How can Human-Centered AI principles be considered when designing an AI-as-a-service platform that democratizes access to personalized healthcare?" This informed both our research direction and investigation. Our approach involves a design fiction methodology, engaging clinicians from different domains to gather their perspectives on how AI can meet their needs by envisioning potential future scenarios and addressing possible ethical and social challenges. Additionally, we incorporate Meta-Design principles, investigating opportunities for users to modify the AI system based on their experiences. This promotes a platform that evolves with the user and considers many different perspectives.

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights.

BACKGROUND: The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. OBJECTIVE: This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. METHODS: Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. RESULTS: The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. CONCLUSIONS: This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

Urban public health spatial planning using big data technology and visual communication in IoT.

The planning of urban public health spatial can not only help people's physical and mental health but also help to optimize and protect the urban environment. It is of great significance to study the planning methods of urban public health spatial. The application effect of traditional urban public health spatial planning is poor, in this paper, urban public health spatial planning using big data technology and visual communication in the Internet of Things (IoT) is proposed. First, the urban public health spatial planning architecture is established in IoT, which is divided into the perception layer, the network layer and the application layer; Second, information collection is performed at the perception layer, and big data technology is used at the network layer to simplify spatial model information, automatically sort out spatial data, and establish a public health space evaluation system according to the type and characteristics of spatial data; Finally, the urban public health space is planned based on the health assessment results and the visual communication design concept through the application layer. The results show that when the number of regions reaches 60,000, the maximum time of region merging is 7.86s. The percentage of spatial fitting error is 0.17. The height error of spatial model is 0.31m. The average deviation error of the spatial coordinates is 0.23, which can realize the health planning of different public spaces.

Designing Digital COVID-19 Screening: Insights and Deliberations.

Due to the global COVID-19 pandemic, public health control and screening measures have been introduced at healthcare facilities, including those housing our most vulnerable populations. These warning measures situated at hospital entrances are presently labour-intensive, requiring additional staff to conduct manual temperature checks and risk-assessment questionnaires of every individual entering the premises. To make this process more efficient, we present eGate, a digital COVID-19 health-screening smart Internet of Things system deployed at multiple entry points around a children's hospital. This paper reports on design insights based on the experiences of concierge screening staff stationed alongside the eGate system. Our work contributes towards social-technical deliberations on how to improve design and deploy of digital health-screening systems in hospitals. It specifically outlines a series of design recommendations for future health screening interventions, key considerations relevant to digital screening control systems and their implementation, and the plausible effects on the staff who work alongside them.

IoT-based wearable health monitoring device and its validation for potential critical and emergency applications.

The COVID-19 pandemic brought the world to a standstill, posing unprecedented challenges for healthcare systems worldwide. The overwhelming number of patients infected with the virus placed an enormous burden on healthcare providers, who struggled to cope with the sheer volume of cases. Furthermore, the lack of effective treatments or vaccines means that quarantining has become a necessary measure to slow the spread of the virus. However, quarantining places a significant burden on healthcare providers, who often lack the resources to monitor patients with mild symptoms or asymptomatic patients. In this study, we propose an Internet of Things (IoT)-based wearable health monitoring system that can remotely monitor the exact locations and physiological parameters of quarantined individuals in real time. The system utilizes a combination of highly miniaturized optoelectronic and electronic technologies, an anti-epidemic watch, a mini-computer, and a monitor terminal to provide real-time updates on physiological parameters. Body temperature, peripheral oxygen saturation (SpO2), and heart rate are recorded as the most important measurements for critical care. If these three physiological parameters are aberrant, then it could represent a life-endangering situation and/or a short period over which irreversible damage may occur. Therefore, these parameters are automatically uploaded to a cloud database for remote monitoring by healthcare providers. The monitor terminal can display real-time health data for multiple patients and provide early warning functions for medical staff. The system significantly reduces the burden on healthcare providers, as it eliminates the need for manual monitoring of patients in quarantine. Moreover, it can help healthcare providers manage the COVID-19 pandemic more effectively by identifying patients who require medical attention in real time. We have validated the system and demonstrated that it is well suited to practical application, making it a promising solution for managing future pandemics. In summary, our IoT-based wearable health monitoring system has the potential to revolutionize healthcare by providing a cost-effective, remote monitoring solution for patients in quarantine. By allowing healthcare providers to monitor patients remotely in real time, the burden on medical resources is reduced, and more efficient use of limited resources is achieved. Furthermore, the system can be easily scaled to manage future pandemics, making it an ideal solution for managing the health challenges of the future.

European IoT Use in Homes: Opportunity or Threat to Households?

The residential sector is characterized by new digital challenges. The Internet of Things (IoT) is a key-driver of innovation and operations management. This study aims to measure and assess IoT devices at the level of individuals, which are households, in European countries. For this scope, through the multi-criteria decision analysis (MCDA), we analyse data from Eurostat providing a mix of indicators allowing information to be aggregated at the level of individual Europeans and disaggregated by age group. The results highlight that only four countries (Netherlands, Denmark, Sweden and Malta) are classified as a high cluster in the examined scenarios. The 16-24 age group is the most involved in the uses of IoT devices, but the previous three northern European countries also show very high values for the 35-44 age group. IoT devices serve as a springboard for achieving a powerful propulsion toward technological innovation in the new business models, identifying opportunities and being a way to make many routine tasks more agile. Training programs and awareness campaigns are policy suggestions for the development of IoT devices favouring a cultural change on their use. However, there is an emerging need for studies that monitor environmental health impacts to prevent possible threats.

Research on Detection and Early Warning Mechanism of Emergency Public Health Medical Education System Based on Internet of Things Technology.

Sudden public health and medical education events have tested the stability of society to a great extent. The government need to strengthen capacity building, make use of system dynamic supervision, warn public health events in advance, and minimize the impact scope and related harmfulness of events. This not only facilitates the rapid mobilization of resources by the later government but also facilitates the comprehensive and detailed deployment and arrangement of decision-makers. As we all know, the Internet of Things is used by all walks of life because of its outstanding advantages of low power consumption, low cost, and wide range. Therefore, this article takes the Internet of Things as the technical basis of the system. According to the actual demand and resource design, it includes two system functions: detection and early warning. The results show that: (1) considering the practical principle, the evaluation system interface found that the scores of font size and color style are all below 80%, which need to be optimized and adjusted; the overall interface basically meets the needs of users. (2) The throughput of the three methods is different. The CoAP-E has superior throughput. (3) With the increase in packet loss rate, the request success rate of the CoAP method decreases in a "drop" manner. The CoAP-E method in this article has the best performance. (4) When the packet loss rate is 25%, the network adaptability of this method is the strongest, and the retransmission rate is less than 18%; the CoAP method is as high as 35%. (5) When the number of concurrent requests is less than 2500, there is no obvious difference between the three methods; the optimal performance of the dynamic load balancing method is 10.1 s. (6) The system comprehensively considers various factors of five site selections. The highest comprehensive score of Final Site, 5 is 8.7, which can be used as the resettlement place of emergency rescue facilities. This article starts from the characteristics and needs of public emergencies, and the final set of the system runs well. It can quickly reflect public health emergencies and medical education events. Use the most effective system functions for risk control, and maximize the analysis, organization, and coordination of events. The follow-up optimization of system details needs to be studied.