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Good indoor air quality (IAQ) is critically important for many aspects of our lives, including as we've found recently in reducing the transmission of airborne diseases such as COVID-19. Delivering good IAQ poses several challenges to organisations: it can require changes in working practices, be bounded by infrastructure capabilities such as buildings and their heating and ventilation systems, and result in substantial energy usage. In this study we have conducted a preliminary investigation measuring IAQ in a typical 'science lab' classroom, and engaging with stakeholders to jointly explore these data. Our mixed methods approach uncovers an indoor air quality 'trilemma', which relates air quality, energy usage, and stakeholder practices that can be mediated by, and understood as, a site for potentially impactful future HCI designs. © 2023 Owner/Author.
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The review discusses the diagnostic application of biosensors as point-of-care devices in the COVID-19 pandemic. Biosensors are important analytical tools that can be used for the robust and effective detection of infectious diseases in real-time. In this current scenario, the utilization of smart, efficient biosensors for COVID-19 detection is increasing and we have included a few smart biosensors such as smart and intelligent based biosensors, plasmonic biosensors, field effect transistor (FET) biosensors, smart optical biosensors, surface enhanced Raman scattering (SERS) biosensor, screen printed electrode (SPE)-based biosensor, molecular imprinted polymer (MIP)-based biosensor, MXene-based biosensor and metal-organic frame smart sensor. Their significance as well as the benefits and drawbacks of each kind of smart sensor are mentioned in depth. Furthermore, we have compiled a list of various biosensors which have been developed across the globe for COVID-19 and have shown promise as commercial detection devices. Significant challenges in the development of effective diagnostic methods are discussed and recommendations have been made for better diagnostic outcomes to manage the ongoing pandemic effectively.
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With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, and the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices and cloud computing services, and basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, and generate multivariate data to provide just-in-time healthcare services. In this article, we present a novel collaborative disease detection system based on IoMT amalgamated with captured image data. The system can be based on intelligent agents, where every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared with baseline solutions for disease detection. © 2001-2012 IEEE.
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pH sensors are increasingly being utilized in the biomedical field and have been implicated in health applications that aim to improve the monitoring and treatment of patients. In this work, a previously developed Titanium Nitride (TiN) solid-state pH sensor is further enhanced, with the potential to be used for pH regulation inside the human body and for other biomedical, industrial, and environmental applications. One of the main limitations of existing solid-state pH sensors is their reduced performance in high redox mediums. The potential shift E0 value of the previously developed TiN pH electrode in the presence of oxidizing or reducing agents is 30 mV. To minimize this redox shift, a Nafion-modified TiN electrode was developed, tested, and evaluated in various mediums. The Nafion-modified electrode has been shown to shift the E0 value by only 2 mV, providing increased accuracy in highly redox samples while maintaining acceptable reaction times. Overcoming the redox interference for pH measurement enables several advantages of the Nafion-modified TiN electrode over the standard pH glass electrode, implicating its use in medical diagnosis, real-time health monitoring, and further development of miniaturized smart sensors.
Subject(s)
Tin , Titanium , Humans , Electrodes , Hydrogen-Ion ConcentrationABSTRACT
Communicable diseases spread quickly among people and animals by an infectious medium, including bacteria, viruses, etc. Recently, the world has witnessed the biggest outbreak of the century due to the novel coronavirus (COVID-19) disease. The immaculate amalgamation of sensor technology helps people to deal with such diseases. This led to motivation amongst the researchers to employ smart sensor technologies to detect, prevent, and control the lively growth of such communicable diseases. Smart sensors include biosensors, wearable sensors, unmanned vehicles, bedsheet sensors, etc., which help prevent and control communicable diseases. These smart sensors collect real-time data about the transmissibility of the disease that can be further processed using advanced machine-learning techniques. The huge real-time sensory data help obtain more precise outcomes and expedite the efficient use of smart sensors for diagnosing communicable disease viruses.
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Transportation, logistics, storage, and many other sectors provide a wide space for applying Industry 4.0. This era, with its components, represents the equipment necessary to obtain a unique competitive advantage. Being smart through sensors, big data, and digitalization corresponds not only to evolution but also provides protection for businesses in the face of depression. The COVID-19 pandemic caused collapses and defects for very large enterprises and large enterprises, especially for small and medium-sized enterprises (SMEs). This article focuses on SMEs and their profits from using smart sensors. Thus, the aim was to expose the striking effect of Industry 4.0 on earnings during the crisis in the Visegrad Four. The Mann-Kendall trend was used to map the consequences contrasting the period of 2016-2021. The investigation involved samples from 1221 Slovak, 259 Czech, 855 Polish, and 2156 Hungarian enterprises. The results showed that more than 80% of businesses did not have a negative trend in how their earnings changed over time. This fact was confirmed by a z-test for the comparison of one proportion for each analyzed country. The adaptation to Industry 4.0 strengthened the muscle for bankruptcy resilience during the crisis. In addition, it may encourage enterprises to be smart in the same or different sectors.
Subject(s)
Bankruptcy , COVID-19 , Humans , Pandemics , COVID-19/epidemiology , COVID-19/prevention & control , IndustryABSTRACT
COVID-19 has become a global health concern, and wearing masks is a key measure to curb COVID-19 from rapidly spreading. While COVID-19 patients can be accurately determined using Rapid Antigen and PCR tests, these tests are costly, time-consuming, invasive, and uncomfortable. Further, they should be performed in a specialized environment despite showing the COVID-19 symptoms such as fever, cough, rapid heart rate, shortness of breath, and low blood oxygen saturation level. To this end, this study aims to automatically identify, and track the COVID-19 suspects in real-time by embedding smart sensors to face masks. The mask was developed to gather the data related to five major symptoms of COVID-19: body temperature, cough, heart rate, breathing pattern, and blood oxygen level. Data collected using smart sensors were used to identify and track COVID-19 suspects using Deep Neural Networks, the Internet of Things (IoT), and Artificial Intelligence (AI). Yielded results showed the proposed mask can identify COVID-19 suspects 92% accurately. © 2022 IEEE.
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Efficient battery technology is imperative for the adoption of clean energy automotive solutions. In addition, efficient battery technology extends the useful life of the battery as well as provides improved performance to fossil fuel technology. Model predictive control (MPC) is an effective way to operate battery management systems (BMS) at their maximum capability, while maintaining the safety requirements. Using the physics-based model (PBM) of the battery allows the control system to operate on the chemical and physical process of the battery. Since these processes are internal to the battery and are physically unobservable, the extended Kalman filter (EKF) serves as a virtual observer that can monitor the physical and chemical properties that are otherwise unobservable. These three methods (i.e., PBM, EKF, and MPC) together can prolong the useful life of the battery, especially for Li-ion batteries. This capability is not limited to the automotive industry: any real-world smart application can benefit from a portable/mobile efficient BMS, compelling these systems to be executed on resource-constrained embedded devices. Furthermore, the intrinsic adaptive control process of the PBM is uniquely suited for smart systems and smart technology. However, the sheer computational complexity of PBM for MPC and EKF prevents it from being realized on highly constrained embedded devices. In this research work, we introduce a novel, unique, and efficient embedded software architecture for a PB-EKF-MPC smart sensor for BMS, specifically on embedded devices, by addressing the computational complexity of PBM. Our proposed embedded software architecture is created in such a way to be executed on a 32-bit embedded microprocessor running at 100 MHz with a limited memory of 128 KB, and still obtains an average execution time of 4.8 ms.
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Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible.
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Meat 4.0 refers to the application the fourth industrial revolution (Industry 4.0) technologies in the meat sector. Industry 4.0 components, such as robotics, Internet of Things, Big Data, augmented reality, cybersecurity, and blockchain, have recently transformed many industrial and manufacturing sectors, including agri-food sectors, such as the meat industry. The need for digitalised and automated solutions throughout the whole food supply chain has increased remarkably during the COVID-19 pandemic. This review will introduce the concept of Meat 4.0, highlight its main enablers, and provide an updated overview of recent developments and applications of Industry 4.0 innovations and advanced techniques in digital transformation and process automation of the meat industry. A particular focus will be put on the role of Meat 4.0 enablers in meat processing, preservation and analyses of quality, safety and authenticity. Our literature review shows that Industry 4.0 has significant potential to improve the way meat is processed, preserved, and analysed, reduce food waste and loss, develop safe meat products of high quality, and prevent meat fraud. Despite the current challenges, growing literature shows that the meat sector can be highly automated using smart technologies, such as robots and smart sensors based on spectroscopy and imaging technology.
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Purpose>Many investigations are going on in monitoring, contact tracing, predicting and diagnosing the COVID-19 disease and many virologists are urgently seeking to create a vaccine as early as possible. Even though there is no specific treatment for the pandemic disease, the world is now struggling to control the spread by implementing the lockdown worldwide and giving awareness to the people to wear masks and use sanitizers. The new technologies, including the Internet of things (IoT), are gaining global attention towards the increasing technical support in health-care systems, particularly in predicting, detecting, preventing and monitoring of most of the infectious diseases. Similarly, it also helps in fighting against COVID-19 by monitoring, contract tracing and detecting the COVID-19 pandemic by connection with the IoT-based smart solutions. IoT is the interconnected Web of smart devices, sensors, actuators and data, which are collected in the raw form and transmitted through the internet. The purpose of this paper is to propose the concept to detect and monitor the asymptotic patients using IoT-based sensors.Design/methodology/approach>In recent days, the surge of the COVID-19 contagion has infected all over the world and it has ruined our day-to-day life. The extraordinary eruption of this pandemic virus placed the World Health Organization (WHO) in a hazardous position. The impact of this contagious virus and scarcity among the people has forced the world to get into complete lockdown, as the number of laboratory-confirmed cases is increasing in millions all over the world as per the records of the government.Findings>COVID-19 patients are either symptomatic or asymptotic. Symptomatic patients have symptoms such as fever, cough and difficulty in breathing. But patients are also asymptotic, which is very difficult to detect and monitor by isolating them.Originality/value>Asymptotic patients are very hazardous because without knowing that they are infected, they might spread the infection to others, also asymptotic patients might be having very serious lung damage. So, earlier prediction and monitoring of asymptotic patients are mandatory to save their life and prevent them from spreading.
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COVID-19 and asthma are respiratory diseases that can be life-threatening in uncontrolled circumstances and require continuous monitoring. A poverty-stricken South Asian country like Bangladesh has been bearing the brunt of the COVID-19 pandemic since its beginning. The majority of the country’s population resides in rural areas, where proper healthcare is difficult to access. This emphasizes the necessity of telemedicine, implementing the concept of the Internet of Things (IoT), which is still under development in Bangladesh. This paper demonstrates how the current challenges in the healthcare system are resolvable through the design of a remote health and environment monitoring system, specifically for asthma patients who are at an increased risk of COVID-19. Since on-time treatment is essential, this system will allow doctors and medical staff to receive patient information in real time and deliver their services immediately to the patient regardless of their location. The proposed system consists of various sensors collecting heart rate, body temperature, ambient temperature, humidity, and air quality data and processing them through the Arduino Microcontroller. It is integrated with a mobile application. All this data is sent to the mobile application via a Bluetooth module and updated every few seconds so that the medical staff can instantly track patients’ conditions and emergencies. The developed prototype is portable and easily usable by anyone. The system has been applied to five people of different ages and medical histories over a particular period. Upon analyzing all their data, it became clear which participants were particularly vulnerable to health deterioration and needed constant observation. Through this research, awareness about asthmatic symptoms will improve and help prevent their severity through effective treatment anytime, anywhere.
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In recent years, intelligent sensing has gained significant attention because of its autonomous decision-making ability to solve complex problems. Today, smart sensors complement and enhance the capabilities of human beings and have been widely embraced in numerous application areas. Artificial intelligence (AI) has made astounding growth in domains of natural language processing, machine learning (ML), and computer vision. The methods based on AI enable a computer to learn and monitor activities by sensing the source of information in a real-time environment. The combination of these two technologies provides a promising solution in intelligent sensing. This survey provides a comprehensive summary of recent research on AI-based algorithms for intelligent sensing. This work also presents a comparative analysis of algorithms, models, influential parameters, available datasets, applications and projects in the area of intelligent sensing. Furthermore, we present a taxonomy of AI models along with the cutting edge approaches. Finally, we highlight challenges and open issues, followed by the future research directions pertaining to this exciting and fast-moving field.
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The COVID-19 virus outbreak led to the need of developing smart disinfection systems, not only to protect the people that usually frequent public spaces but also to protect those who have to subject themselves to the contaminated areas. In this paper it is developed a human detector smart sensor for autonomous disinfection mobile robot that use Ultra Violet C type light for the disinfection task and stops the disinfection system when a human is detected around the robot in all directions. UVC light is dangerous for humans and thus the need for a human detection system that will protect them by disabling the disinfection process, as soon as a person is detected. This system uses a Raspberry Pi Camera with a Single Shot Detector (SSD) Mobilenet neural network to identify and detect persons. It also has a FLIR 3.5 Thermal camera that measures temperatures that are used to detect humans when within a certain range of temperatures. The normal human skin temperature is the reference value for the range definition. The results show that the fusion of both sensors data improves the system performance, compared to when the sensors are used individually. One of the tests performed proves that the system is able to distinguish a person in a picture from a real person by fusing the thermal camera and the visible light camera data. The detection results validate the proposed system. © 2021, Springer Nature Switzerland AG.