IoT-based unified child monitoring and security system

In this fast-growing modern era, mothers are likely to work soon after childbirth, which makes it hard for them to render complete care to their child. Hiring a childminder is not just costly but also unsafe, especially during a global pandemic like Covid-19. Child abuse is also a major worry. This paper introduces an IoT-based Unified child monitoring and security system without any third-party involvement, thus addressing the parents' needs and concerns. The proposed system monitors the temperature and heart rate of theinfant, humidity of the room, detects motion and sound produced by the baby and adopts suitable measures to notify the parent such as sending alert messages, live video streaming of the infant or turning on a motor to swing the cradle. This system also monitors the movement of toddlers using GPS-and GSM-enabled wrist-bands and continuously sends their live locations. A buzzer is also interfaced with the band to alert if any stranger is in close proximity with the toddler. This system enables parents to keep a watch on their children remotely and thus ensures the safety of the child from any type of abuse. An added feature of this band is that it also prompts to maintain social distancing from the toddlers. Overall, a reliable, continuous and real-time baby monitoring is ensured by the proposed system. © 2023 Scrivener Publishing LLC. All rights reserved.

Lessons learned from the COVID-19 pandemic: implications for semiprofessional soccer clubs after the crisis- a team study

In 2019-2020 and 2020-2021, the COVID-19 pandemic led to unexpected behavioral restrictions, allowing only the individual training of the athletes. The purpose of the present study was to record the effects of a home-based training program on physical performance at a semi-professional level, after the Covid-19 confinement, in terms of external load. Twenty soccer players from one semi-professional team participated in this study. The data was collected by GPS devices, with an accelerometer and gyroscope, and a sampling rate 10Hz. The external load is evaluated by the total distance, the high-intensity runs, the sprint distance as well as the number of accelerations and decelerations. Three matches before and twelve games after the lockdown were analyzed and compared. During the confinement period, the players performed 5-6 training sessions per week. This period lasted over 4 four months. The training sessions were monitored by a free smartphone application. Similarly, the players communicated with the technical staff with a free internet application. The results showed significant increases (p < 0.05) in the total distance covered during the matches after the intervention. No significant increase in high-intensity runs and the number of accelerations were found (p > 0.05). The present data suggest that an intervention monitored by a free application could improve athletic performance at the semi-professional level, even after long-term abstinence from team training such as quarantine or off-season periods. These data might provide affordable solutions to the semi-professional soccer teams, which could be used during the off-season period leading to reduce detraining effects and higher performance in the forthcoming championship.

Mawidi: Smart Appointments Booking System for Social Distancing

In traditional systems of banks, the booking process requires the client to be in the same place, the client withdraws a numbered paper from an electronic device and then sits in the waiting area until his/her number appears on the screen. However, these systems may cause many problems such as wasting clients' time, overcrowding in the waiting area, slow workflow, etc. In this paper, a smart appointment booking system is developed to solve the problems of traditional booking systems of banks and achieve social distancing. The proposed system applies Quick Response (QR) code, Global Positioning System (GPS), and Bluetooth Low Energy (BLE) technologies to improve workflow and achieve social distancing in banks. The proposed system is developed on two sides. On the client-side, a mobile application is developed, a QR is generated for the user, which contains booking information, and GPS is used to determine the location of the client as it is only possible to book if he/she is within 100 meters from the bank. Due to the restrictions imposed caused by the spread of COVID-19, BLE technology works to ensure social distancing between clients. On the employee side, a website is created to enable the employee to deal with the client. The proposed system is expected to reduce problems related to traditional systems, gain client satisfaction, facilitate workflow for employees, and contribute to reduce the spread of COVID-19. © 2023 NSP Natural Sciences Publishing Cor.

Blockchain-enabled COVID-19 Contact Tracing Solutions

In light of the fast-spreading number of COVID-19 cases worldwide, contact tracing proved to be an effective measure to slow down the infection rate and mitigate the casualties caused by this virus. However, because of several concerns in terms of privacy, as well as security, several countries and their population around the globe are reluctant to adopt contact tracing solutions to contain the spread of the virus. Because of its distributed, public, and auditable nature, blockchain can be a groundbreaking solution contact tracing, given that it would provide a privacy-oriented contact tracing solution. Therefore, in this chapter, we discuss and compare the two alternatives proposed by the BeepTrace framework, active and passive, and also present some initial results based on an early implementation of it. As it can be seen, by utilizing blockchain together with contact tracing, user privacy, security, and decentralization can be guaranteed, giving back the trust needed for these applications to work. © 2022 by John Wiley & Sons Ltd. All rights reserved.

Monintoring Student Depression by Mobile Devices with Autoencoder Networks

The timely assessment of mental health is difficult since we lack the objective measurements of symptoms, especially for the Covid-19 pandemic quarantined students. Fortunately, smart phones can capture the real-world data such as the GPS traces and the phone active time et.al that link the behavioral patterns to the mental health. However, recent studies are based on a very small size of participants and only collect fewer phone features, which means that the effective predicting models which require various features are hardly adopted. In this paper, we develop an android application to record multidimensional data of users as well as a PHQ-9 and a SAS questionary, and we distribute it to 176 college students to collect larger scale data when in quarantine period. To address the unprecise problem of handcrafted feature extraction, we design an autoencoder machine learning model to monitor the student mental health. Extensive experiments indicate that the performance of the proposed method improves its F-1 score for PHQ-9 and SAS by 5% and 6% to the state of the current studies, respectively. © 2023 IEEE.

Autonomous Flight Using UWB-Based Positioning System with Optical Flow Sensors in a GPS-Denied Environment

This study presents the positioning method and autonomous flight of a quadrotor drone using ultra-wideband (UWB) communication and an optical flow sensor. UWB communication obtains the distance between multiple ground stations and a mobile station on a robot, and the position is calculated based on a multilateration method similar to global positioning system (GPS). The update rate of positioning using only UWB communication devices is slow;hence, we improved the update rate by combining the UWB and inertial measurement unit (IMU) sensor in the prior study. This study demonstrates the improvement of the positioning method and accuracy by sensor fusion of the UWB device, an IMU, and an optical flow sensor using the extended Kalman filter. The proposed method is validated by hovering and position control experiments and also realizes a sufficient rate and accuracy for autonomous flight.

"To our health!” Perceived benefits offset privacy concerns in using national contact-tracing apps

PurposeCommunity health is placed under the limelight during the COVID-19 crisis, providing a unique context for investigating citizens' health-privacy tradeoff in accepting social surveillance technology. To elucidate this tradeoff dilemma, an extended privacy calculus framework integrated with the Health Belief Model, legislative protection, and individual collectivism was examined using the case of national contact-tracing apps.Design/methodology/approachThe hypotheses were tested through PLS-SEM analysis with data collected from a survey on Bluezone – a national app in Vietnam.FindingsThe results indicated the negative impact of privacy concerns, which was offset by the positive effect of perceived benefits in using contact-tracing apps. The effect size of perceived benefits on usage frequency was twice as large as that of privacy concerns. Individual collectivism was revealed as a mitigator of the tradeoff dilemma, as it was positively associated with perceived benefits, whereas legislative protection had no such role. Citizens may perceive legislation protection as invalid when the technologies are developed, implemented, and monitored by the authorities.Originality/valueThe theoretical contributions lie in the extension of the privacy calculus model as well as its application in the context of mobile health apps and surveillance technology. The study empirically corroborated that the privacy calculus theory holds when technologies move along the pervasiveness spectrum. This study also provided actionable insights for policymakers and developers who advocate the mass acceptance of national contact-tracing apps.

Experimental Implementation of COVID-19 Safety Measures in Ride-Sharing Cabs Using Deep Learning and Internet of Things

Increased urbanization and on-demand mobility have resulted in the boon of many ride-sharing companies. Besides providing faster, economical, and comfortable rides, these rideshares are also environment-friendly as they save a lot of energy. This research work presents a model which would be beneficial for the passengers riding these carpools as it would not only help to curb the spread of infection in current pandemic but also detect whether the driver is drowsy or not to prevent possible road accidents. The proposed web application includes three detections based on novel deep learning algorithms implementing face recognition, facemask, and drowsiness detection of the driver with an alert mechanism to send immediate email alerts to the company and driver. The novelty of the proposed application is that the current and live status of the driver is continuously recorded using latest technologies like convolutional neural networks (CNNs), histogram of oriented gradients (HOG), support vector machine (SVM) classifier, and computer vision. In addition to this, a real-time vehicle tracking device is also implemented using Node MCU, Global Positioning System (GPS) module, and Blynk app to keep the company updated about the real-time location of the rideshare. The name of the recognized driver is displayed as output. Face mask and drowsiness detection is done with an accuracy of 99%, and the real-time location of the cab is indicated in Google Maps on the Blynk app. The proposed web application would be very beneficial for ride-sharing companies in the current COVID situation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Geometrical Tolerances Android Application Developed for Student for a Better Understanding of Orientation Tolerances According to the Latest ISO Standards

In the year 2021, a virtual reality training application has been developed, specifically for the Oculus Quest 2 headset, in order to allow the users to view and analyse 3D models for a wide variety of geometrical tolerances, tolerance zones and even the conformity condition and the datum features. This application allows the students to better understand the geometrical tolerances in accordance with the latest ISO GPS standards by using different 3D interactive models in order to highlight different types of geometrical tolerances and tolerance zones. In order to try to make the virtual reality training application more mobile and accessible for students, a mobile application, designed for the android operating system smartphones, has been developed. This application can facilitate better learning outcomes within the teaching-learning process by enabling the students to visualize a wide variety of 3D models representing different types of geometrical tolerances. Due to the dramatic evolution of technology in the past 20 years and the need to constantly keep up with it, the learning and teaching process suffered a massive change. The Covid pandemic has closed Universities all over the world and so, for the teaching process to continue, a sudden shift from traditional teaching to a more modern one involving the digital world was needed. Thanks for the fact that, nowadays, students are more inclined to use different types of mobile devices, the shift to the new learning paradigm has not left a great "scar” on student and teachers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COUNTERSAVIOR: AIoMT and IIoT-Enabled Adaptive Virus Outbreak Discovery Framework for Healthcare Informatics

In the current pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes global positioning system (GPS) spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual's mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject's cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behavior, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behavior patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3-D tracker movements of individuals, 3-D contact analysis of COVID-19 and suspected individuals for 24 h, forecasting and risk classification of COVID-19, suspected and safe individuals.

Congruence Mapping of the Activity Flows Allocated in Built Environments: A Pilot Application of Under-Development Software in an Emergency-Care Service

Featured ApplicationThis paper presents the application of a software program that is currently under development that provides feedback for the mapping activities that are carried out in built environments and analyses the congruence in the relationship between the flow of activities and their environments. Exemplary results were obtained in the case study of a healthcare emergency facility, although it is possible to apply this software in other types of complex environments. The obtained data allow facility managers to prioritize and reallocate activities when a change is required. It also shows unmapped relationships. It is important to investigate these data because they can indicate failures in the mapping process and can provide an opportunity to obtain a more complete understanding of the allocation and flow of activities. These data can also help us to identify points of conflict or opportunities for adjustment in the allocation of activities in order to improve the flow of activities.Due to the large number of activities that must be carried out by emergency-care services (ESs), the tasks of facility managers and architects are challenging and complex. Several strategies, guides, and diagnoses have already been developed in order to improve ESs. Part of the solution to this problem depends on obtaining a normative and universal understanding of the problem, and another part depends on conducting a specific and relational analysis between the environment and the flow of activities that are allocated within it. This paper presents the results of a study that was conducted using a software program that is currently under development for mapping the congruence relationship between activities and environments. Here, we present a discussion of the first results that were obtained with the instrument, which was applied to a single case. For this purpose, the fundamentals of the instrument, as well as the environment and the flows of an ES at a university hospital, are described. The forms of analysis, benefits, and limitations of the instrument were investigated, with a view towards its use in supporting the management and the design of large and complex environments, such as emergency departments. In this program, the relationships that are hidden from the managers, the designers, and the researchers due to the aforementioned complexity are revealed through the use of matrices. This mapping can supplement the decision making of the managers and the designers. The application showed advantages in modeling with fewer inputs, mainly in pre-design evaluations.

Wild goose chase: Geese flee high and far, and with aftereffects from New Year's fireworks

In the present Anthropocene, wild animals are globally affected by human activity. Consumer fireworks during New Year (NY) are widely distributed in W-Europe and cause strong disturbances that are known to incur stress responses in animals. We analyzed GPS tracks of 347 wild migratory geese of four species during eight NYs quantifying the effects of fireworks on individuals. We show that, in parallel with particulate matter increases, during the night of NY geese flew on average 5–16 km further and 40–150 m higher, and more often shifted to new roost sites than on previous nights. This was also true during the 2020–2021 fireworks ban, despite fireworks activity being reduced. Likely to compensate for extra flight costs, most geese moved less and increased their feeding activity in the following days. Our findings indicate negative effects of NY fireworks on wild birds beyond the previously demonstrated immediate response.

Sanitization Using Autonomous Drone

Since the COVID -19 epidemic has nearly brought about global catastrophe, every chance to make things better must be considered. One such technique for improvement is airborne decontamination. Researching this method's efficacy in the pandemic is vital since it can be used for surface cleaning of bigger areas. There are numerous instances of using drones to disinfect areas affected by epidemics, but best practices and factors affecting effectiveness have not yet been found. The adaptable uses of agricultural drones are evident from reports about utilizing drones for disinfection during a pandemic. The authors of this study calculated the potential amount of disinfectant fluid per unit area using various parameters for fly speed, flight altitude, and flow rate. As a result, by adjusting the settings, a range of disinfectant concentrations per unit area can be provided. Even though the results create a lot of new queries, they can be used to determine appropriate flying characteristics based on various disinfection liquids. © 2022 IEEE.

Using geospatial technologies to manage COVID-19

The new environmental and biological developments in the light of increased socioeconomic deprivation, along with the persistence of inadequate living environments, have had an effect on the pattern of disease transmission. These cultural, economic and political changes intensify epidemiological instability, favoring the arrival of new pathogens and the re-emergence of old infectious diseases, traditionally linked to remote populations and theoretically occurring in new environments. An analysis of the scientific information obtained from the implementation of this approach to the study of endemic diseases such as COVID-19 disease is ongoing. The geographical, temporal and spectral resolution of the Remote Sensing satellite vehicle sensor will capture the emergence of various epidemics. The satellites will glow the globe during a specific health emergency. Various navigation and synchronization systems, vision systems and satellite communication systems have confirmed to become powerful instruments to support disease control efforts during outbreak of Coronavirus in the world. Efficient combination of remote sensing and other technologies such as GPS (Global Positioning System) and GIS (Geographic Information System) that can locate events in space adds skilled information to identify vulnerable habitats. These methods are characterized by a reasonably low cost, thereby providing valuable information that are used to research those endemic diseases and supporting surveillance and control activities rarely developed previously. This chapter seeks to assess the opportunities for the application of these technologies and their roles for the study and control of the most common and actually the most harmful disease. © 2023 Elsevier Inc. All rights reserved.

A decision support model for selecting unmanned aerial vehicle for medical supplies: context of COVID-19 pandemic

PurposeIn recent times, due to rapid urbanization and the expansion of the E-commerce industry, drone delivery has become a point of interest for many researchers and industry practitioners. Several factors are directly or indirectly responsible for adopting drone delivery, such as customer expectations, delivery urgency and flexibility to name a few. As the traditional mode of delivery has some potential drawbacks to deliver medical supplies in both rural and urban settings, unmanned aerial vehicles can be considered as an alternative to overcome the difficulties. For this reason, drones are incorporated in the healthcare supply chain to transport lifesaving essential medicine or blood within a very short time. However, since there are numerous types of drones with varying characteristics such as flight distance, payload-carrying capacity, battery power, etc., selecting an optimal drone for a particular scenario becomes a major challenge for the decision-makers. To fill this void, a decision support model has been developed to select an optimal drone for two specific scenarios related to medical supplies delivery.Design/methodology/approachThe authors proposed a methodology that incorporates graph theory and matrix approach (GTMA) to select an optimal drone for two specific scenarios related to medical supplies delivery at (1) urban areas and (2) rural/remote areas based on a set of criteria and sub-criteria critical for successful drone implementation.FindingsThe findings of this study indicate that drones equipped with payload handling capacity and package handling flexibility get more preference in urban region scenarios. In contrast, drones with longer flight distances are prioritized most often for disaster case scenarios where the road communication system is either destroyed or inaccessible.Research limitations/implicationsThe methodology formulated in this paper has implications in both academic and industrial settings. This study addresses critical gaps in the existing literature by formulating a mathematical model to find the most suitable drone for a specific scenario based on its criteria and sub-criteria rather than considering a fleet of drones is always at one's disposal.Practical implicationsThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.Social implicationsThe proposed methodology incorporates GTMA to assist decision-makers in order to appropriately choose a particular drone based on its characteristics crucial for that scenario.Originality/valueThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.

Detection of anomalies in cycling behavior with convolutional neural network and deep learning

Background: Cycling has always been considered a sustainable and healthy mode of transport. With the increasing concerns of greenhouse gases and pollution, policy makers are intended to support cycling as commuter mode of transport. Moreover, during Covid-19 period, cycling was further appreciated by citizens as an individual opportunity of mobility. Unfortunately, bicyclist safety has become a challenge with growing number of bicyclists in the 21st century. When compared to the traditional road safety network screening, availability of suitable data for bicycle based crashes is more difficult. In such framework, new technologies based smart cities may require new opportunities of data collection and analysis. Methods: This research presents bicycle data requirements and treatment to get suitable information by using GPS device. Mainly, this paper proposed a deep learning-based approach "BeST-DAD” to detect anomalies and spot dangerous points on map for bicyclist to avoid a critical safety event (CSE). BeST-DAD follows Convolutional Neural Network and Autoencoder (AE) for anomaly detection. Proposed model optimization is carried out by testing different data features and BeST-DAD parameter settings, while another comparison performance is carried out between BeST-DAD and Principal Component Analysis (PCA). Result: BeST-DAD over perform than traditional PCA statistical approaches for anomaly detection by achieving 77% of the F-score. When the trained model is tested with data from different users, 100% recall is recorded for individual user's trained models. Conclusion: The research results support the notion that proper GPS trajectory data and deep learning classification can be applied to identify anomalies in cycling behavior. © 2023, The Author(s).

Aggregate Learning for Mixed Frequency Data

Large and acute economic shocks such as the 2007-2009 financial crisis and the current COVID-19 infections rapidly change the economic environment. In such a situation, real-time analysis of regional heterogeneity of economic conditions using alternative data is essential. We take advantage of spatio-temporal granularity of alternative data and propose a Mixed-Frequency Aggregate Learning (MF-AGL) model that predicts economic indicators for the smaller areas in real-time. We apply the model for the real-world problem;prediction of the number of job applicants which is closely related to the unemployment rates. We find that the proposed model predicts (i) the regional heterogeneity of the labor market condition and (ii) the rapidly changing economic status. The model can be applied to various tasks, especially economic analysis. © 2022 IEEE.

The relationship between Twitter sentiment and mobility during the COVID-19 pandemic

The COVID-19 pandemic has affected public behavior in a variety of ways. Concerns about the spread of a hitherto unknown virus drove numerous changes in public behavior, including a greater tendency to self-isolate at home. In this study, we assigned numerical scores to key sentiments expressed in COVID-19-related posts on major social media platform Twitter to measure changes in public sentiment during the pandemic. We also examined the relationship between mobility in various locations around Japan and scores for sentiments such as dislike and fear. Our research provided evidence of a tendency for mobility to decline (i.e. for more people to self-isolate at home) roughly one month after scores for negative public sentiment regarding COVID-19 increased. Mobility is closely connected with a variety of economic activities, mainly in service industries. This suggests that the sentiment in Twitter postings on COVID-19 that we discuss in this study is a leading indicator of changes in mobility (the extent to which people self-isolate at home), demonstrating the effectiveness of Twitter data in forecasting short-term changes in economic activity during the pandemic. © 2022 IEEE.

Tracking Changes in Mobility Before and After the First SARS-CoV-2 Vaccination Using Global Positioning System Data in England and Wales (Virus Watch): Prospective Observational Community Cohort Study.

BACKGROUND: Evidence suggests that individuals may change adherence to public health policies aimed at reducing the contact, transmission, and spread of the SARS-CoV-2 virus after they receive their first SARS-CoV-2 vaccination when they are not fully vaccinated. OBJECTIVE: We aimed to estimate changes in median daily travel distance of our cohort from their registered addresses before and after receiving a SARS-CoV-2 vaccine. METHODS: Participants were recruited into Virus Watch starting in June 2020. Weekly surveys were sent out to participants, and vaccination status was collected from January 2021 onward. Between September 2020 and February 2021, we invited 13,120 adult Virus Watch participants to contribute toward our tracker subcohort, which uses the GPS via a smartphone app to collect data on movement. We used segmented linear regression to estimate the median daily travel distance before and after the first self-reported SARS-CoV-2 vaccine dose. RESULTS: We analyzed the daily travel distance of 249 vaccinated adults. From 157 days prior to vaccination until the day before vaccination, the median daily travel distance was 9.05 (IQR 8.06-10.09) km. From the day of vaccination to 105 days after vaccination, the median daily travel distance was 10.08 (IQR 8.60-12.42) km. From 157 days prior to vaccination until the vaccination date, there was a daily median decrease in mobility of 40.09 m (95% CI -50.08 to -31.10; P<.001). After vaccination, there was a median daily increase in movement of 60.60 m (95% CI 20.90-100; P<.001). Restricting the analysis to the third national lockdown (January 4, 2021, to April 5, 2021), we found a median daily movement increase of 18.30 m (95% CI -19.20 to 55.80; P=.57) in the 30 days prior to vaccination and a median daily movement increase of 9.36 m (95% CI 38.6-149.00; P=.69) in the 30 days after vaccination. CONCLUSIONS: Our study demonstrates the feasibility of collecting high-volume geolocation data as part of research projects and the utility of these data for understanding public health issues. Our various analyses produced results that ranged from no change in movement after vaccination (during the third national lock down) to an increase in movement after vaccination (considering all periods, up to 105 days after vaccination), suggesting that, among Virus Watch participants, any changes in movement distances after vaccination are small. Our findings may be attributable to public health measures in place at the time such as movement restrictions and home working that applied to the Virus Watch cohort participants during the study period.

Early coronavirus disease detection using internet of things smart system

The internet of things (IoT) is quickly evolving, allowing for the connecting of a wide range of smart devices in a variety of applications including industry, military, education, and health. Coronavirus has recently expanded fast across the world, and there are no particular therapies available at this moment. As a result, it is critical to avoid infection and watch signs like fever and shortness of breath. This research work proposes a smart and robust system that assists patients with influenza symptoms in determining whether or not they are infected with the coronavirus disease (COVID-19). In addition to the diagnostic capabilities of the system, the system aids these patients in obtaining medical care quickly by informing medical authorities via Blynk IoT. Moreover, the global positioning system (GPS) module is used to track patient mobility in order to locate contaminated regions and analyze suspected patient behaviors. Finally, this idea might be useful in medical institutions, quarantine units, airports, and other relevant fields.