ABSTRACT
Rapid improvements in healthcare services and affordable IoT in the past decade have been a big help in dealing with the issue of fewer medical facilities. Unfortunately, some people still choose not to get immunized, thus fear and reluctance remain a part of human existence despite widespread vaccination initiatives. Therefore, it is important to screen this group of potential spreaders as soon as possible since they may become infected and transfer viruses to others. It is in this context that the pharmaceutical sector might benefit from highly developed health monitoring systems. This work has created and tested a multi-node architecture based on Fog computing to perform real-time initial screening and recording of such individuals, therefore addressing the demand and reducing the unpredictability of the scenario. In addition to capturing photographs of the subject's face, the suggested device also recorded the subject's current body temperature and GPS locations. As an added bonus, the suggested system could upload information to a remote server over the internet. To test the viability of the proposed system, a thorough examination of the existing work environment was carried out, including implementation and evaluations. From the results of the statistical analysis, it was seen that the suggested IoT Fog-enabled ecosystem may be put to good use. © 2023 IEEE.
ABSTRACT
Corona Virus (COVID-19) has already done havoc in the world. More than six hundred million people suffered from this virus and six million people are dead amongst them in the world. In Bangladesh, two million people have tested positive and approximately 30 thousand people are dead. SARS-CoV-2 virus causes this infectious corona disease. When an infected individual sneezes, coughs, speaks, or breathes, the virus is disseminated from their mouth or nose. One can also be infected by touching contaminated surface and spreads more in indoor environment. So, it has taught us the necessity of washing and sanitizing in our daily affairs. Automatic boot spray machine is a very practical and useful instrument to fight against the corona virus. This contact free spray machine helps to sanitize the boot without getting in contact with the surface and ultimately helps to prevent the spread of corona virus. The purpose of this essay is to create a boot sprayer that automatically releases soapy water. At a distance of meters, an obstruction sensor is employed to identify the heat and presence of the boot. A p-n-p transistor is used to operate the machine. This machine works perfectly at a distance of 2-30cm. It also provides good sensitivity percentage. This setup is user convenient and it saves money and power. © 2023 IEEE.
ABSTRACT
The global COVID-19 outbreak has had a wide-ranging impact on people's lives. This research looks at the recent shift in consumer preferences toward contact-free shopping when purchasing fashion goods. Push-pull-mooring (PPM) theory is used to identify and predict factors that promote or hinder a shift toward contact-free shopping. A survey of young consumers is used to develop and test hypotheses. The findings show that the PPM factors have a significant effect on switching behavior, risk perception, perceived value, and lock-in factors (along with some sub-factors related to each) being significantly related to both intentions to switch to contact-free shopping and actual switching behavior. Theoretical, managerial, and societal implications are discussed in the context of digital wellbeing. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
The popularity of the Internet and smartphones has led to the rapid development of mobile payment. Because of its fast, convenient and contact-free characteristics, it has accelerated the utilization rate of mobile payment in the context of the raging of COVID-19. Even so, merchants in traditional business circles in Taiwan still do not provide mobile payment services. Secondly, most of the current literature on mobile payment is still discussing the usage behavior of consumers, and there are few related researches on merchants. Therefore, this study takes merchants in the Dadaocheng business district of Taipei City as the research object, and explores the influence of subjective norms on the introduction of mobile payment by traditional merchants through one-to-one interviews. The research result is that the handling fee will increase the operating cost, affect the profit of the store, and become the main obstacle to the implementation. However, merchants will pay attention to the use of mobile payment by surrounding stores, and will affect the decision of store A as to whether to install. © 2022 ACM.
ABSTRACT
Purpose: The aim of this paper is to explore the changes in the ICT and global value chains (GVCs) after the COVID-19 pandemic. Design/methodology/approach: This study compared the difference between Korea' domestic ICT industries, ICT imports and ICT exports before and after the COVID-19 outbreak by using trade data of ICT products and national economic indicators, and presents growth strategy for the ICT industry in the post-COVID 19 era. For this purpose, this study determined the causalities between Korea's imports/exports of ICT products and composite Indexes before and after COVID-19, and derived implications in the ICT industry environment after the COVID-19 pandemic. Findings: Analysis results showed the following changes in Korea's ICT industry in the post-COVID-19 world. (1) Non-face-to-face and contact-free technologies related sectors in the ICT industry, such as the semiconductor sector, have grown exponentially;(2) as the USA has grown as the new key player, the causal relationship with China, a key player of the GVC in the pre-COVID-19 era, disappeared;and (3) the GVC of the ICT industry is not a rigid one-way vertical structure, but is changing to a flexible structure influenced by cooperation and competition between countries. Originality/value: The results indicate that it is essential to constantly develop new ICT sectors that make use of non-face-to-face and contact-free technologies in the post-COVID-19 era, and the main strategies in response to the changed GVC would be taking the initiative by securing source technologies and expanding through cooperation with other GVCs and resource sharing. © 2022, Emerald Publishing Limited.
ABSTRACT
SpO2, also known as blood oxygen saturation, is a vital physiological indicator in clinical care. Since the outbreak of COVID-19, silent hypoxia has been one of the most serious symptoms. This symptom makes the patient's SpO2 drop to an extremely low level without discomfort and causes medical care delay for many patients. Therefore, regularly checking our SpO2 has become a very important matter. Recent work has been looking for convenient and contact-free ways to measure SpO2 with cameras. However, most previous studies were not robust enough and didn't evaluate their algorithms on the data with a wide SpO2 range. In this paper, we proposed a novel non-contact method to measure SpO2 by using the weighted K-nearest neighbors (KNN) algorithm. Five features extracted from the RGB traces, POS, and CHROM signals were used in the KNN model. Two datasets using different ways to lower the SpO2 were constructed for evaluating the performance. The first one was collected through the breath-holding experiment, which induces more motion noise and confuses the actual blood oxygen features. The second dataset was collected at Song Syue Lodge, which locates at an elevation of 3150 meters and has lower oxygen concentration in the atmosphere making the SpO2 drop between the range of 80% to 90% without the need of holding breath. The proposed method outperforms the benchmark algorithms on the leave-one-subject-out and cross-dataset validation. © 2022 IEEE.
ABSTRACT
The global COVID-19 outbreak has had a wide-ranging impact on people’s lives. This research looks at the recent shift in consumer preferences toward contact-free shopping when purchasing fashion goods. Push–pull–mooring (PPM) theory is used to identify and predict factors that promote or hinder a shift toward contact-free shopping. A survey of young consumers is used to develop and test hypotheses. The findings show that the PPM factors have a significant effect on switching behavior, risk perception, perceived value, and lock-in factors (along with some sub-factors related to each) being significantly related to both intentions to switch to contact-free shopping and actual switching behavior. Theoretical, managerial, and societal implications are discussed in the context of digital wellbeing.
ABSTRACT
Amidst this uncertain situation of the Covid pandemic happening worldwide, It is necessary to protect the health workers too. Since they are more prone to the dangerous virus than any other. To reduce the risk of virus infection spreading to Health care workers, this paper proposes the concept of wearing a smart Healthwear band, an IoT monitoring device integrated with a mobile application for monitoring the patients with a contact-less mechanism. Healthware is placed on the Covid infected patients which sense the temperature, pulse, and oxygen level of the patient. This was monitored manually before with the equipment present in the hospitals. This Health wear device automatically collects the data of patients and alerts the Nurses and doctors in case of emergencies. This reduces the contact of Healthcare workers with infected patients. This paper briefly describes the working of the IoT enabled healthcare device and the methodology used in detail. The cost of the proposed device is very less compared to other existing systems. © 2022 IEEE.
ABSTRACT
Blood oxygen saturation (SpO(2)) is an important indicator forpulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO(2). Most of these works are contact-based, requiring users to cover a phone's camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO(2) monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO(2) information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO(2) prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.
ABSTRACT
A contact-free continuous heart rate variability (HRV) analysis is required to conduct daily heart monitoring and minimize physical contact during medical remedies owing to COVID-19. This paper suggests a Doppler cardiogram (DCG) signal processing and reconstruction system that enables the standard deviation of normal-to-normal peaks (SDNN) obtained from DCG to be used as an actual HRV index. The heartbeat signals of twelve healthy adults were recorded. Three electrodes and a Doppler radar module were used to record the electrocardiogram (ECG) and DCG signals, respectively. To optimize the performance of the signal reconstruction system, two signal processing methods were applied to the dataset. These DCG signals were reconstructed into a signal that mimicked the ECG using a variational autoencoder (VAE), to enhance the consistency of the SDNN. The synthetic signal quality was assessed by comparing the SDNN of the synthetic ECG with that of the reference ECG. A total of 1,430 signals were reconstructed to achieve a valid SDNN. A unified analysis of the signal reconstruction results using different signal processing methods was built up to raise the consistency growth. The final result of the signal reconstruction system represented a consistency improvement of 75.5%, compared to the SDNN of the input DCG.
ABSTRACT
Collision-free or contact-free routing through connected networks has been actively studied in the industrial automation and manufacturing context. Contact-free routing of personnel through connected networks (e.g., factories, retail warehouses) may also be required in the COVID-19 context. In this context, we present an optimization framework for identifying routes through a connected network that eliminate or minimize contacts between randomly arriving agents needing to visit a subset of nodes in the network in minimal time. We simulate the agent arrival and network traversal process, and introduce stochasticity in travel speeds, node dwell times, and compliance with assigned routes. We present two optimization formulations for generating optimal routes-no-contact and minimal-contact-on a real-time basis for each agent arriving to the network given the route information of other agents already in the network. We generate results for the time-average number of contacts and normalized time spent in the network. © 2021 IEEE.
ABSTRACT
Contact-free sensors offer important advantages compared to traditional wearables. Radio-frequency sensors (e.g., radars) offer the means to monitor cardiorespiratory activity of people without compromising their privacy, however, only limited information can be obtained via movement, traditionally related to heart or breathing rate. We investigated whether five complex hemodynamics scenarios (resting, apnea simulation, Valsalva maneuver, tilt up and tilt down on a tilt table) can be classified directly from publicly available contact and radar input signals in an end-to-end deep learning approach. A series of robust k-fold cross-validation evaluation experiments were conducted in which neural network architectures and hyperparameters were optimized, and different data input modalities (contact, radar and fusion) and data types (time and frequency domain) were investigated. We achieved reasonably high accuracies of 88% for contact, 83% for radar and 88% for fusion of modalities. These results are valuable in showing large potential of radar sensing even for more complex scenarios going beyond just heart and breathing rate. Such contact-free sensing can be valuable for fast privacy-preserving hospital screenings and for cases where traditional werables are impossible to use.