An Intelligent Collaborative Image-Sensing System for Disease Detection

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.

SHAREABLE RESOURCE: New Year, Wiser You

The article presents the discussion on preparing a renewed health plan and absolve to change poor habits. Topics include pandemic accelerated many trends in the health and fitness industry and taught many lessons;and walking, running, hiking, biking, swimming, racquet sports, and seasonal sports being readily accessible.

i-Sheet: A Low-Cost Bedsheet Sensor for Remote Diagnosis of Isolated Individuals.

In this article, we propose a smart bedsheet-i-Sheet-for remotely monitoring the health of COVID-19 patients. Typically, real-time health monitoring is very crucial for COVID-19 patients to prevent their health from deteriorating. Conventional healthcare monitoring systems are manual and require patient input to start monitoring health. However, it is difficult for the patients to give input in critical conditions as well as at night. For instance, if the oxygen saturation level decreases during sleep, then it is difficult to monitor. Furthermore, there is a need for a system that monitors post-COVID effects as various vitals get affected, and there are chances of their failure even after the recovery. i-Sheet exploits these features and provides the health monitoring of COVID-19 patients based on their pressure on the bedsheet. It works in three phases: 1) sensing the pressure exerted by the patient on the bedsheet; 2) categorizing the data into groups (comfortable and uncomfortable) based on the fluctuations in the data; and 3) alerting the caregiver about the condition of the patient. Experimental results demonstrate the effectiveness of i-Sheet in monitoring the health of the patient. i-Sheet effectively categorizes the condition of the patient with an accuracy of 99.3% and utilizes 17.5 W of the power. Furthermore, the delay involved in monitoring the health of patients using i-Sheet is 2 s which is very diminutive and is acceptable.

A portable, low-cost and high-throughput electrochemical impedance spectroscopy device for point-of-care biomarker detection

This paper presents a portable, fast and accurate electrochemical impedance spectroscopy (EIS) device with 8-well interdigitated electrode chips for biomarker detection. The design adopts low crest factor multisine signal synthesis at low frequencies (<1 kHz) and single-tone signals at high frequencies (>1 kHz), which significantly increases measurement speed without sacrificing accuracy. In addition, the low excitation amplitude of 10 mV preserves impedance linearity and protects the biosamples. The system achieved an average magnitude accuracy error of 0.30% in the frequency range of interest and it requires only 0.46 s to scan 28 frequency points from 10 Hz to 1 MHz. Experiments were conducted to test the capability to detect antibodies against SARS-CoV-2. Gold nanoparticles bound with protein G (GNP-G) were employed as the conjugated secondary antibody probe to detect anti-SARS-CoV-2 IgG in serum. A highly statistical significance (p = 7×10−6) could be found in the impedance data at 10 kHz. The impedance magnitude alteration caused by the GNP-G of the positive and negative groups were 27.2%±13.6% and 4.1%±1.7%, respectively. The results imply that the proposed system enables rapid COVID-19 antibody biomarker detection. Moreover, the EIS system and GNPs have the potential to be modified to detect other biomarkers. © 2022 The Author(s)

An ultra-sensitive luteolin sensor based on Co-doped nitrogen-containing carbon framework/MoS2−MWCNTs composite for natural sample detection

As a common antioxidant and antimicrobial agent in plants, luteolin has a variety of pharmacological activities and biological effects, the ability to specifically bind proteins and thus inhibit novel coronaviruses and treat asthma. Here, Co doped nitrogen-containing carbon frameworks/MoS2−MWCNTs (Co@NCF/MoS2−MWCNTs) nanocomposites have been synthesized and successfully applied to electrochemical sensors. X-ray photoelectron spectroscopy, scanning electron microscopy and X-ray diffraction were used to examine the morphology and structure of the samples. Meanwhile, the electrochemical behavior of Co@NCF/MoS2−MWCNTs was investigated. Due to its excellent electrical conductivity, electrocatalytic activity and adsorption, it is used for the detection of luteolin. The Co@NCF/MoS2−MWCNTs/GCE sensor can detect luteolin in a linear range from 0.1 nM to 1.3 μM with a limit of detection of 0.071 nM. Satisfactory results were obtained for the detection of luteolin in natural samples. In addition, the redox mechanism and electrochemical reaction sites of luteolin were investigated by the scan rate of CV curves and density functional theory. This work demonstrates for the first time the combination of ZIF-67-derived Co@NCF and MoS2−MWCNTs as electrochemical sensors for the detection of luteolin, which opens a new window for the sensitive detection of luteolin. © 2022 Elsevier Ltd

Fifth international conference on materials and environmental science: Role of new materials in sustainable development

The Fifth International Conference on Materials and Environmental Science (ICMES20221), is an interdisciplinary platform to promote a multi-sectoral and collaborative approach in the field of development of new and innovative approaches in materials, their applications in energy and renewable energy, environmental science, sustainable development, health, biotechnology and electrical engineering. The scientific committee of ICMES2022 agreed that the health session was the priority since the Covid19 pandemic still constitutes a Public Health Emergency of International Concern. There are many multifunctional materials available by the advent of nanotechnology, ranging from carbon nanotubes, graphene, inorganic nanoparticles, conducting polymers, 2D materials, CO2 material capture, etc… Materials science Conference is an event that brings together leading researchers spanning the field of materials science and engineering to present and discuss cutting edge research with other experts in the field: exchanging ideas to advance current understanding towards the future of materials science. © 2022

Worldwide Survey of Fitness Trends for 2023

The annual ACSM's Health & Fitness Journal® worldwide survey to determine industry trends by health and fitness professionals is now in its 17th consecutive year. The COVID-19 pandemic certainly made an impact on the 2021 survey and continued for 2022, but for 2023, some current trends are emerging whereas others are weakening because of the world's recovery from the isolation caused by COVID-19. The #1 trend for 2023, as it was for 2022, is wearable technology. Home exercise gyms was #2 for 2022 but has dropped to #13 for 2023. Fitness programs for older adults will make a comeback in 2023, breaking the top 10 at #4. Functional fitness training, a popular form of exercise for the older adult, is the #5 trend for 2023. Apply It!: From this article, the reader should understand the following concepts: • Explain the differences between a fitness fad and a fitness trend • Use the worldwide fitness trends in the commercial, corporate, clinical (including medical fitness), and community health and fitness industry to further promote physical activity • Study expert opinions about identified fitness trends for 2023

Electronic Sensors to Detect SARS-CoV-2 Viruses in Real Time

Sensors with 60 nm gap junctions coated with aptamers that bind with S1 and S2 spiking proteins of the SARS-CoV-2 virus were developed. Sensor impedance changes with virus enabling rapid (∼1 min), point-of-care detection. Exosomes and other nanoparticles in the saliva produce false positive signals but do not bind with aptamers and are easily removed to achieve 6% false positivity rates. A positive sensor voltage is used to attract negatively charged SARS-CoV-2 viruses to the junction and reduce sensor false negativity rates to below 7%. The limit of detection of the sensor is 1000 viruses and can be altered by changing the sensor's lateral dimensions and its transduction noise level. © 2001-2012 IEEE.

Image Recognition Based on Deep Learning with Thermal Camera Sensing

As the COVID-19 epidemic spread across the globe, people around the world were advised or mandated to wear masks in public places to prevent its spreading further. In some cases, not wearing a mask could result in a fine. To monitor mask wearing, and to prevent the spread of future epidemics, this study proposes an image recognition system consisting of a camera, an infrared thermal array sensor, and a convolutional neural network trained in mask recognition. The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen. The proposed system reduces the inefficiency of traditional object detection by providing training data according to the specific needs of the user and by applying You Only Look Once Version 4 (YOLOv4) object detection technology, which experiments show has more efficient training parameters and a higher level of accuracy in object recognition. All datasets are uploaded to the cloud for storage using Google Colaboratory, saving human resources and achieving a high level of efficiency at a low cost. © 2023 CRL Publishing. All rights reserved.

Scheelite-type rare earth vanadates TVO4 (T = Ho, Y, Dy) electrocatalysts: Investigation and comparison of T site variations towards bifunctional electrochemical sensing application

In the wake of the recent COVID-19 pandemic, antibiotics are now being used in unprecedented quantities across the globe, raising major concerns regarding pharmaceutical pollution and antimicrobial resistance (AMR). In view of the incoming tide of alarming apprehensions regarding their aftermath, it is critical to investigate control strategies that can halt their spread. Rare earth vanadates notable for their fundamental and technological significance are increasingly being used as electrochemical probes for the precise quantification of various pharmaceutical compounds. However, a comprehensive study of the role of the cationic site in tailoring the response mechanism is relatively unexplored. Hence, in this work we present a facile hydrothermal synthesis route of rare earth vanadates TVO4 (T = Ho, Y, Dy) as efficient electrocatalyst for the simultaneous detection of nitrofurazone (NF) and roxarsone (RX). There appears to be a significant correlation between T site substitution, morphological and the electrochemical properties of rare earth metal based vanadates. Following a comparative study of the electrochemical activity, the three rare-earth vanadates were found to respond differently depending on their composition of T sites. The results demonstrate that Dy-based vanadate displays increased electrical conductivity and rapid charge transfer characteristics. Thus, under optimal reaction conditions DyVO4- based electrodes imparts outstanding selectivity towards the detection of NF and RX with an extensive detection window of NF = 0.01–264 µM & RX = 0.01–21 µM and 36–264 µM and low detection limit (0.002, 0.0009 µM for NF and RX, respectively). In real-time samples, the proposed sensor reveals itself to be a reliable electrode material capable of detecting residues such as NF and RX. © 2022 Elsevier B.V.

A comprehensive review on graphene FET bio-sensors and their emerging application in DNA/RNA sensing & rapid Covid-19 detection

In recent years, the significance of biosensors has increased rapidly due to the growing demand for rapid detection of various biomarkers with high selectivity and sensitivity. Among different biosensors, Graphene Field Effect Transistor (Gr-FET) based biosensors has emerged as a promising device and exhibited wide range of application prospects. Gr-FET biosensors are ideal for ultra-sensitive immunological diagnosis applications as it can sense surrounding changes on their surface with low noise. Recently Gr-FET based biosensors have gained profound research interest among scientific community because of its ability in detection of SARS-CoV-2 (corona virus-2). This review article highlights the sensing performance and characteristics of different Gr-FET biosensors like DNA sensor, RNA sensor, glucose sensor, lactose sensor, protein sensor, pH sensor, various bacteria and virus detecting sensors etc.This article also critically reviews the recent progress in Gr-FET based SARS- CoV-2 covid-19 virus detection bio-sensors. © 2022 Elsevier Ltd

Domestic Violence Detection Using Smart Microphones

Domestic violence between partners and family members is a worldwide problem increasing every day. As per academic studies and media articles, it escalated during the COVID-19 outbreak. Domestic violence can portray verbally and physically in several ways (for instance, between partners or against children and older people). Deep Learning (DL) combined with the Internet of Things (IoT) technology could support the detection of domestic violence, which is one of many societal issues. This paper describes a system that uses a Deep Learning model and smart microphones to identify domestic violence. The datasets used are from the Google AudioSet (GA) and from the Toronto Emotional Speech Set (TESS). For the training of the dataset, the system used spectrograms and MFCCs (Mel-Frequency Cepstral Coefficients). The system employs two approaches: (i) an ANN (Artificial Neural Network) model, and (ii) a ResNet model. The Resnet model obtained an accuracy of 71%. The ANN model, which brought an accuracy of 83%, was tested and loaded to a Raspberry Pi, i.e., connected to the microphone for audio recording. The recorded audio was fed to the trained model, classifying the audio, and alerting the domestic violence to relatives, friends, or volunteers registered with the system via e-mail. The designed system is compact and can be placed inside the home. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Real-Time Health Monitoring System Using Predictive Analytics

Medical specialists are primarily interested in researching health care as a potential replacement for conventional healthcare methods nowadays. COVID-19 creates chaos in society regardless of the modern technological evaluation involved in this sector. Due to inadequate medical care and timely, accurate prognoses, many unexpected fatalities occur. As medical applications have expanded in their reaches along with their technical revolution, therefore patient monitoring systems are getting more popular among the medical actors. The Internet of Things (IoT) has met the requirements for the solution to deliver such a vast service globally at any time and in any location. The suggested model shows a wearable sensor node that the patients will wear. Monitoring client metrics like blood pressure, heart rate, temperature, etc., is the responsibility of the sensor nodes, which send the data to the cloud via an intermediary node. The sensor-acquired data are stored in the cloud storage for detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Among the different cloud-based predictive models now being used, the model having the highest accuracy will be treated as the resultant model. This resultant model will be further used for the data dissemination mechanism by which the concerned medical actors will be provided an alert message for a proper medication in a desirable manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Nanomaterials and Biosensing Technology for the SARS-CoV-2 Detection

The outbreak of corona virus disease 2019 (COVID-19) has aroused great attention around the world. SARS-CoV-2 possesses characteristics of faster transmission, immune escape, and occult transmission by many mutation, which caused still grim situation of prevention and control. Early detection and isolation of patients are still the most effective measures at present. So, there is an urgent need for new rapid and highly sensitive testing tools to quickly identify infected patients as soon as possible. This review briefly introduces general characteristics of SARS-CoV-2, and provides recentl overview and analysis based on different detection methods for nucleic acids, antibodies, antigens as detection target. Novel nano-biosensors for SARS-CoV-2 detection are analyzed based on optics, electricity, magnetism, and visualization. In view of the advantages of nanotechnology in improving detection sensitivity, specificity and accuracy, the research progress of new nano-biosensors is introduced in detail, including SERS-based biosensors, electrochemical biosensors, magnetic nano-biosensors and colorimetric biosensors. Functions and challenges of nano-materials in construction of new nano-biosensors are discussed, which provides ideas for the development of various coronavirus biosensing technologies for nanomaterial researchers.

PROMPT: Process Mining and Paravector Tensor-Based Physical Health Monitoring Framework

The provision of physical healthcare services during the isolation phase is one of the major challenges associated with the current COVID-19 pandemic. Smart healthcare services face a major challenge in the form of human behavior, which is based on human activities, complex patterns, and subjective nature. Although the advancement in portable sensors and artificial intelligence has led to unobtrusive activity recognition systems, very few studies deal with behavior tracking for addressing the problem of variability and behavior dynamics. In this regard, we propose the fusion of PRocess mining and Paravector Tensor (PROMPT)-based physical health monitoring framework that not only tracks subjective human behavior, but also deals with the intensity variations associated with inertial measurement units. Our experimental analysis of a publicly available dataset shows that the proposed method achieves 14.56% better accuracy in comparison to existing works. We also propose a generalized framework for healthcare applications using wearable sensors and the PROMPT method for its triage with physical health monitoring systems in the real world. © 2001-2012 IEEE.

A Secure Wearable Framework for Stress Detection in Patients Affected by Communicable Diseases

The emergence of COVID-19 has drastically altered the lifestyle of people around the world, resulting in significant consequences on people's physical and mental well-being. Fear of COVID-19, prolonged isolation, quarantine, and the pandemic itself have contributed to a rise in hypertension among the general populace globally. Protracted exposure to stress has been linked with the onset of numerous diseases and even an increased frequency of suicides. Stress monitoring is a critical component of any strategy used to intervene in the case of stress. However, constant monitoring during activities of daily living using clinical means is not viable. During the current pandemic, isolation protocols, quarantines, and overloaded hospitals have made it physically challenging for subjects to be monitored in clinical settings. This study presents a proposal for a framework that uses unobtrusive wearable sensors, securely connected to an artificial intelligence (AI)-driven cloud-based server for early detection of hypertension and an intervention facilitation system. More precisely, the proposed framework identifies the types of wearable sensors that can be utilized ubiquitously, the enabling technologies required to achieve energy efficiency and secure communication in wearable sensors, and, finally, the proposed use of a combination of machine-learning (ML) classifiers on a cloud-based server to detect instances of sustained stress and all associated risks during times of a communicable disease epidemic like COVID-19. © 2001-2012 IEEE.

Detection of Potential Vulnerable Patients Using Oximeter

Agriculture, education and health systems have all progressed in the last decade. In times of pandemic crises like COVID-19, IoT and sensors play a critical role in the medical industry. Sensors and IoT-based health care gadgets have emerged as saviors for humanity in the face of resource shortage. Pulse oximeters are one such instrument that has been utilized widely during pandemics. Since a long time, pulse oximeters have been used to measure crucial body functions such as saturation of peripheral oxygen (SpO2) and pulse rate. They have been utilized to detect vital signs in patients in order to diagnose cardiac trouble early. However, oximeters have been widely utilized to detect SPO2 levels in persons during the current pandemic. People are being attacked by the COVID-19, which is silently destroying their lungs, causing pneumonia and lowering oxygen levels to dangerously low levels. We propose a strategy in this study for detecting possibly vulnerable individuals by classifying them using data obtained from pulse oximeters. We propose an approach by involving volunteers who will record their vitals and share it with administrators on a regular basis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Portable HEPA filtration successfully augments natural-ventilation-mediated airborne particle clearance in a legacy design hospital ward.

As the severe acute respiratory syndrome coronavirus-2 pandemic has proceeded, ventilation has been recognized increasingly as an important tool in infection control. Many hospitals in Ireland and the UK do not have mechanical ventilation and depend on natural ventilation. The effectiveness of natural ventilation varies with atmospheric conditions and building design. In a challenge test of a legacy design ward, this study showed that portable air filtration significantly increased the clearance of pollutant aerosols of respirable size compared with natural ventilation, and reduced spatial variation in particle persistence. A combination of natural ventilation and portable air filtration is significantly more effective for particle clearance than either intervention alone.

Real-time Air Quality Monitoring Using Optical Sensors to Prevent Severe Acute Respiratory Syndromes (SARS)

This work presents the development, test, and validation of a system that gathers and analyses data from optical sensors to monitor the air quality of indoor environments to help prevent Severe Acute Respiratory Syndromes (SARS). © Optica Publishing Group 2022 The Authors.

A Framework for Infectious Disease Monitoring With Automated Contact Tracing-A Case Study of COVID-19

Throughout human history, deadly infectious diseases emerged occasionally. Even with the present-day advanced healthcare systems, the COVID-19 has caused more than six million deaths worldwide (as of 27 July 2022). Currently, researchers are working to develop tools for better and effective management of the pandemic. "Contact tracing " is one such tool to monitor and control the spread of the disease. However, manual contact tracing is labor-intensive and time-consuming. Therefore, manually tracking all potentially infected individuals is a great challenge, especially for an infectious disease like COVID-19. To date, many digital contact tracing applications were developed and used globally to restrain the spread of COVID-19. In this work, we perform a detailed review of the current digital contact tracing technologies. We mention some of their key limitations and propose a fully integrated system for contact tracing of infectious diseases using COVID-19 as a case study. Our system has four main modules-1) case maps;2) exposure detection;3) screening;and 4) health indicators that take multiple inputs like users' self-reported information, measurement of physiological parameters, and information of the confirmed cases from the public health, and keeps a record of contact histories using Bluetooth technology. The system can potentially evaluate the users' risk of getting infected and generate notifications to alert them about the exposure events, risk of infection, or abnormal health indicators. The system further integrates the Web-based information on confirmed COVID-19 cases and screening tools, which potentially increases the adoption rate of the system.