Social Distancing Monitoring and Contact Tracing on CCTV Footage

Social distancing is one of the most effective measures to prevent the spread of the COVID-19 disease. Most methods of enforcing this in the Philippines resort to manual methods. As such, a video-based social distancing monitoring tool can help ensure constant enforcement of social distancing due to the availability and up-time of CCTV cameras in various areas. This can be achieved by using object detection and tracking techniques. Object detection can be used to detect people within an area, and tracking can be used to watch people who get into close contact with one another. Contact tracing can also be performed by processing the social distancing measurements and tracking information. This information can be stored to keep a record of who has a high risk of infection based on who they came into contact with and for how long. We introduce a social distancing monitoring and contact tracing framework using the EfficientDet object detector and DeepSORT tracker. This framework is used to monitor social distancing violations and keep a record of violations associated to the tracked people. © 2022 IEEE.

Crowd Density Estimation Using Neural Network for COVID-19 and Future Pandemics

The population's vulnerability is exacerbated by the lack of effective treatment drugs and immunity to COVID-19. The only viable strategy for combating this pandemic is social separation. In order to automate the task of monitoring social separation using surveillance footage, this study presents a neural network-based crowd density estimation for COVID-19 and future pandemics. The suggested framework employs the object identification model to distinguish persons in the scene, as well as the deep sort technique to track recognized people with issued IDs. The obtained results of the proposed work are compared in terms of loss values defined by object classification and localization, frames per second (FPS), and mean average precision (mAP). The proposed method yields good results against faster region-based convolutional neural network (RCNN) and single-shot detector (SSD). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

System for Evaluation of COVID-19 Prevention Guidelines Compliance

In a catastrophic medical situation caused by an infectious disease, such as COVID-19, it is very important to quickly determine who and where to be tested and supervised. The current COVID-19 screening test is conducted by identifying people with high probability of infection, such as who made direct or indirect contact with the confirmed person, by identifying the moving path of the confirmed person. Currently, various methods are being employed, such as interviewing or location tracking through cell phone forensics, to determine the moving path of the confirmed person. Mostly, however, these methods are time consuming, inaccurate, and easy to invade privacy while promptness, accuracy and anonymity are key values of epidemiological surveillance. There is still no preemptive management methods for a space where infection occurs are possible. Investigation and action on the area where the infection occurred are just carried out only after a confirmed person has been confirmed. In order to solve these problems, it is necessary to develop an automatic system for evaluating space for compliance of infectious disease prevention guidelines, or simply risk estimation system, using artificial intelligence and computer vision technology. In this paper, we discuss the system for evaluation of COVID-19 prevention guidelines compliance which has been researching and developing by ASSIST. © 2022 IEEE.

Population Index and Analysis Based on Different Geographies, Using Distance Measurement, Social Distancing, and Deep Learning

Nowadays, every individual is familiar with the COVID-19 pandemic which has caused great turmoil in everyone's life. Also, they are aware that there is no medicine or drug to cure COVID immediately, and people are at the risk of losing their lives. Lack of vaccines or delay in vaccine production for mass results social distancing being the only measure to tackle this pandemic. As a result, social distancing has proven to be a very reliable and efficient way to diminish the growth of this disease;the reason why lockdowns are imposed, and people are asked to keep some distance from each other, for their safety as there will be minimal physical contact. Machine learning and artificial intelligence come into the picture in every solution to a generic problem the community faces nowadays like in medical, supply chain management, face detection, etc. Using the power of AI algorithms, the paper aims to develop a robust system to monitor and analyze social distance measurement protocols at public places during the COVID-19 pandemic with the help of CCTV feed and check whether they abide by the safety protocols or not by measuring the distance between them. The proposed approach is implemented to enumerate the number of violations at a popular public place to prevent massive crowds at particular periods. The proposed method is suitable to construct a scrutiny system at a public place to alert people and eschew mass gatherings that can be concluded using achieved results. The paper also has an analysis of the performance of different models of R-CNN, Fast R-CNN, and YOLO. YOLO architectures are validated based on object detection and object tracking rate in real time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Design and Development of Cardiopulmonary Health Monitoring System using IoMT

In this work, an attempt has been made to develop an Internet-of-Medical-Things (IoMT) based tracking system for cardiopulmonary disease affected patients. COVID-19 pandemic has caused a wide range of illness and organ failure to human beings around the world. Severe infection could alter the heart function and result in damage of the cardiac muscles. The disease could cause lung inflammation, pneumonia, severe acute respiratory syndrome and sometimes resulting in death. To monitor the wellness of heart and lungs of patients recovering from COVID-19, an efficient wearable health tracking system is required. The proposed system consists of a sensor to monitor heart rate and Oxygen Saturation (SpO2) of an individual. The sensor data is sent to an Arduino UNO Microcontroller. The normal range of heart rate and oxygen saturation are set as threshold in the control unit. If the heart rate and SpO2 levels are above or below the threshold, the control module will send an alert to the nearby nursing station and provide treatment to the patient. The patient data is continuously recorded and stored in the hospital's cloud as electronic health record. The prototype was tested in individuals in relaxed state and after performing tasks such as stair climbing. Results indicate that the proposed system could accurately track the status of the heart function along with oxygen saturation levels. Hence, this could be used as a cardiopulmonary health tracking device thereby assisting the clinicians in providing appropriate critical care services to the infected patients. © 2022 IEEE.

NFC tag-based mHealth Patient Healthcare Tracking System

Patient misdiagnosis is quite a common occurrence in hospitals around the world. It is a mistake that can happen to anyone at any time, and especially during a pandemic crisis when hospital facilities are overwhelmed with increasing number of patients. This problem could stem from improper patient identification whereby patient files are mislabelled or placed in an incorrect patient dossier. It is the responsibility of a hospital and its employees to guarantee that such mistakes do not occur. With respect to this, near-field communication (NFC) technology, which is a short ranged wireless communication technology, has been identified to have great potential to help identify patients in hospitals.This paper demonstrates a solution by designing and developing a patient healthcare management information system that sees the seamless integration of the NFC technology along with and both web and mobile technologies,to provide a holistic solution to tackle the problem of patient misidentification in a hospital environment especially during pandemics such as COVID-19.The Technology Acceptance Model (TAM) was used as evaluation method in order to quantify the proposed systems's usability and acceptance using 5 constructs. Results showed acceptance of the system with a mean score of 4, indicating that the NFC tag-based mHealth Patient Healthcare Tracking System was found to be useful and easy to use. © 2022 IEEE.

Travel Log Analysis Tracking System Based On Salesforce For Covid-19

This review aims to examine the writing in order to assist specialists and scientists in better understanding and dealing with the impact of Coronavirus on the tourism sector. The study examines the situations and questions created as a result of the pandemic to see why and how Coronavirus - 19 has impacted people's lives. As a result, the report identifies the qualities, establishments, and preconceptions that the travel industry should question, as well as the activities that should be made to take a step ahead. The study also looks into the considerable loss the travel sector is experiencing throughout the Coronavirus stages and proposes a solution based on the Salesforce platform to address some of the issues. This provides an overview of how the Coronavirus affects the travel business, as well as recommendations for the industry, examining and settling some of them with 'Travel Log Analysis utilizing Salesforce'. © 2022 IEEE.

Accurate and Contactless COVID-19 Fever Screening and Attendance Tracking System

Over the past two years, COVID-19 has led to and is still leading to lots of deaths to date. Many industries have been affected by that, and governments have united in finding ways to mitigate the spread of the disease, thus leading life to return to normal. There are several ways that were followed to do that, such as social distancing, thermal screening, and virtual communication. Thermal screening has proven its practicality in certain entities that require face-to-face contact. Researchers have been contributing to finding effective ways to develop screening methods to help re-accelerate the learning process. This paper proposes a fever screening system to record and track individuals' temperature and an attendance tracking system for educational institutions. The system measures the individual's temperature and records it, and saves their attendance in a database. After completing the measurement taking of an individual, the system uses a buzzer to inform the following individual that it is their turn. This allows the institution to monitor any temperature spikes among the individuals while recording their attendance without close contact at the entrance. Our results validate the usefulness and potential of our system as a fever screening and attendance tracking tool. It also opens the door for further development, allowing regular operation in educational institutions during any upcoming pandemics. © 2022 IEEE.

Ultrasonic Sensor Based Physical Distance and Temperature Tracking System

Now a days maintaining the social distance has become mandatory to decrease the spread of corona. So a novel way of finding pairs automatically of people in a crowded environment that does not participate in the block of public space, that is, about 6 feet of space among them. This Will-making method does not think about crowded traffic or pedestrian directions. Here a moving robot with sensory inputs, a camera to perform non-collision navigation Jump and measure the distance among the adjacent people found in the camera view field. Moreover, this equips the robot with a hot camera that transmits hot wireless images to safety / health workers who watches when someone shows a higher temperature than required. In these situations, the robot integrated with static cameras to improve social distance maintenance Remote Culprits detected, precisely following pedestrians etc. Social segregation measures are important to reduce the spread of Covid. In order to break the chain of transmission, public distribution is strictly followed as usual. This paper centralizes a useful thing to monitor the populated such as ATMs, supermarkets and hospitals for any violations of social segregation. By using the system, I have proposed will be possible to monitor queue world who maintain social isolation in a protected area and to alert individuals in the event. © 2022 IEEE.

Covid-19 Interior Security Tracking System Based on the Artificial Intelligence

This article presents an inexpensive artificial intelligence solution aimed at increasing indoor safety of COVID-19, including a number of important aspects: (1) breakdown of the process (2) Method for mask identification (3). Assessment methodology of social distancing The Arduino Uno sensor system uses an infrasound sensor or heat camera, whereas the Raspberry Pi is equipped with computer vision technologies for mask detection and social distance checks. Indoor measures are the most prevalent—people with a high body heat should stay at home, masks should be worn, and their distance should be at least 1.5–2 m. In the first case, the Arduino Uno temperature sensor board is utilized, while we utilize a single-board Pi Raspberry computer coupled with camera for two additional situations, using computer vision techniques. Due to their compact size and cost, we chose to utilize these devices. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lightweight Security Framework for IoT Enabled Tracking of COVID-19 and its Variants

COVID-19 became the headlines of every nation because of its unrivalled transmission speed and outbreaks in humans, but its new variants are raising new challenges after every few months iteratively. Keeping track of travel history and maintaining a record of people coming in contact with any COVID affected individual has become a prime concern to control the spread of this pandemic disease. The existing tracking system lags in fetching records of people who came in contact with the affected one and maintaining travel history across the local boundaries of the cities. In this position paper, we are proposing a framework for an IoT driven blockchain (BC) based secured tracking system that gathers users' travel and meeting history, and it may help with remote health monitoring. The data gathered for the same is treated as immutable and achieves interoperability with the help of Smart Contracts (SC). It could be proved as a useful framework for post COVID-19 economic revival with the help of an IoT driven blockchain based secured model for remote health monitoring & chain tracking. © 2021 IEEE.

Privacy Concerned on Contact-Tracing Application during COVID-19

The COVID-19 pandemic has greatly affected humanity by destabilizing the world economy through strain on hospital systems and deaths. Medical personnel is working around the clock to establish vaccines. On the other hand, technology contributes to the fight against the virus by tracking COVID-19 infections. Many digital contact tracking smartphone applications have been created to address this epidemic successfully. However, the applications lack transparency, raising worries about their privacy. Contact tracing has been employed to stop the spread of the disease. When battling the coronavirus epidemic, computerized contact tracking has quickly emerged as an essential tool. Therefore, the research conducted in this paper focuses on the challenges of tracking applications to analyze the perspective view of privacy issues. Besides, the paper proposes policies for data privacy to aid in making the tracking applications more effective and successful. © 2022 IEEE.

YOLOv3 based Real Time Social Distance Violation Detection in Public Places

The prevalent COVID 19 pandemic is incessantly taking toll on the lives of people throughout the world. Moreover, the dearth of effectual remedies has caused an expeditious rise in the total COVID 19 cases. Though vaccines have been developed, the enormous task of vaccinating a large population is still challenging. Also, as new variants emanate, the resilience from infections conceivably decreases. Hence, it's most unlikely that we'll achieve herd immunity globally so soon. Thus, since the transmission of COVID causing coronavirus roots mainly to social proximity between people, it is necessary to stringently comply to the non pharmaceutical preventive measures of wearing masks and maintaining physical distancing. Howbeit, it has evidently been found that people are being lethargically ignorant to the social distancing norms with passing time. Hence, an autonomous mechanism intended at social distancing violation detection through monitoring of people is needed to be introduced at an authority level. In this paper, the implementation of YOLO Object detection transfer learning process has been used for accomplishing this aim of real time detection of social distancing violation. Our social distance prediction approach uses a pre-trained YOLOv3 object tracking algorithm for identifying people in an input video stream. A Distance estimation algorithm is further used, that works by computing euclidean distance between the centroids of each pair of detected people. This approach highlights the people violating the social distancing criteria as well as calculates the number of times social distancing gets violated as any two people get closer than a set threshold value of minimum permissible distance. A number of experiments on various pre-recorded video streams has been conducted in order to estimate the viability of this method. Through experimental outcomes, it has been found that this YOLO based object detection method with the proposed social distance prediction algorithm produces favourable results for tracking social distancing in public spaces. © 2021 IEEE.

An Intelligent Online Attendance Tracking System Through Facial Recognition Technique Using Edge Computing

Recently, due to COVID-19 pandemic, the classes, seminars, meetings are scheduled on virtual platform. It is a need to keep track of the presence of attendees. Earlier online attendance involved extracting the list of attendees, which was inconvenient as a lot of people mute themselves and leave the meeting altogether. Therefore, a tool is required to capture attendance through facial recognition which can effectively identify the attendees who remain online for the whole duration of the lecture. In this paper, a method has been proposed to completely automate the attendance tracking system using the concept of edge computing. The tool runs alongside any video conference platform and tracks the faces of attendees in a random interval, and using face recognition technique, find out the people who remain present for the complete duration of the class. This novel method acts as a fail-proof method to monitor attendance and improve digital transparency. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Vision-Based Hand Tracking System Development for Non-Face-to-Face Interaction

Human-computer interaction (HCI) focuses on the interaction between humans and computers and it exists ubiquitously in our daily lives, especially in post COVID era where non-face-to-face interaction is common. Since HCI usually uses a physical controller such as a mouse or a keyboard, it hinders National User Interface, giving a middle ground between the user and the computer. This paper presents a vision-based hand tracking system development for non-face-to-face interaction, which aims to improve HCI by being able to track the hand which will act as the pen and functioning as a reusable writing surface for creating texts, drawings, and such as well as removing or erasing using the user's hand as the pen, and utilizing Open Computer Vision Library (OpenCV) and Mediapipe. Using the computer's camera the hand will be tracked as the pen for creating basic drawings and handwriting. The vision-based board where the user can draw on and the pen or marker will be the user's hand. The results indicate that this system is accurate enough to be a feasible application for handwriting ad basic drawings. © 2021 IEEE.

Proposed Iris Movement Tracking System to Improve the Security of Online Examinations in Nigeria

The covid-19 global pandemic brought about a drastic change to how the world approaches education. The world bank noted that at the peak of the pandemic, 45 countries in Europe and Asia closed schools. A separate source also noted that over 250 million primary and secondary school children were out of school due to the lockdown of schools worldwide. Albeit temporarily, the severity of the pandemic transitioned the method of teaching for most schools from physical to virtual. This paper aims to address one of the limitations that arose due to this transition: online examination security. Examination malpractice was noticed to spike for examinations taken online, even with trained proctors watching test-takers via video conference platforms such as Zoom or Microsoft Teams. Within this research analysis is a planned iris movement tracking system to add another layer of security to online examination systems. This system uses a three-tier style structure. Tier 1 is verification that it is indeed a registered student taking the examination. This will be achieved using facial recognition. Tier 2 involves the storage and protection of facial data of students. The last tier involves the continuous use of webcams to detect the iris movement of students and alert proctors if a student looks away from the screen for a significant amount of time. This paper mainly focuses on tier 3. © 2021 IEEE.

Global Level Smart Vaccination Tracking System using Blockchain and IoT

The COVID-19 outbreak highlighted the smart healthcare infrastructure requirement to speed up vaccination and treatment. Present vaccination supply chain models are fragmented in nature, and they are suitable for a pandemic like COVID-19. Most of these vaccination supply chain models are cloud-centric and depend on humans. Due to this, the transparency in the supply chain and vaccination process is questionable. Moreover, we con't trace where the vaccination programs are facing issues in real-time. Furthermore, traditional supply chain models are vulnerable to a single point of failure and lack people-centric service capabilities. This paper has proposed a novel supply chain model for COVID-19 using robust technologies such as Blockchain and the Internet of Things. Besides, it automates the entire vaccination supplication chain, and it records management without compromising data integrity. We have evaluated our proposed model using Ethereum based decentralized application (DApp) to showcase its real-time capabilities. The DApp contains two divisions to deal with internal (intra) and worldwide (inter) use cases. From the system analysis, it is clear that it provides digital records integrity, availability, and system scalability by eliminating a single point of failure. Finally, the proposed system eliminates human interference in digital record management, which is prone to errors and alternation. © 2021 IEEE.All rights reserved.

Sensor-Based Emotion Tracking System for Computer Games

The game development industry is among the leading industries globally, and in 2020, gaming emerged as a popular entertainment activity upon the COVID-19 outbreak. Thus, competition among gaming companies is high. Hence, they try to adopt new technologies often. Gaming brings multiple feelings for the gamer. At times, the conditions may get even worse from the game's end where the gamer may end up venting out his rage and annoyance. Hence, there is a massive possibility for the gamer to switch to another game which may result in the company to lose its customers. In that scenario, this system can monitor the emotional states of the gamer while playing and manipulate the gaming environment, sound environment, enemy behavior, and gamer mechanism according to the emotional state of the gamer. The sensor-based emotion tracking system identifies the gamer's emotional state using facial emotions, detected through a webcam and heart rate, detected through sensors. The development was carried out through the machine learning models, open cv, Arduino techniques, and reactive programming. The emotional state and facial emotions that will be tracked will count to an accuracy of above 95%. Through that, the target will be to make the gamer satisfied by building appreciation for the services given and by improving the gamer's gaming experience and retain the gamer with the game provider. © 2021 IEEE.

Alleviation of COVID by means of Social Distancing & Face Mask Detection Using YOLO V4

This paper consists of social distancing & face mask detection for the events of coronavirus, alleviation in such pandemic can be solved by social distancing as well as putting on a face mask. This small step of wearing a face mask as well as following social distancing would save lots of lives as the spread of the virus could be mitigated. YOLO stands for You Only Look Once, this algorithm is used for Object Detection as well as Object Tracking, this research uses YOLO for calculating the social distancing & identifying face mask on people’s face with the help of Object Detection, whereas tracking the face is done by Object Tracking. The minimum distance to keep while adhering for social distancing is 6 Feet, keeping this as the base for calculating distance, the model was trained and used for object detection as well as for object tracking. There are different types of algorithms available, YOLO stands out from all the other present currently. The custom datasets were used for the understanding the face masks and it was trained on those datasets for detection and tracking. For evaluation of the trained model, mAP (Mean Average Precision) was calculated for both the use cases (Social Distancing & Face Mask Detection), it works by comparing the ground-truth bounding box vs the detected box and, in the end, returns the score. The higher the mAP score would be, the better model is in the detection of objects. Mean Average Precision was calculated for two different thresholds (0.25 % & 0.50 %) with 101 recall points. Three different classes were created for classification those were Good, Bad & None, for which True Positive & False Positive values were calculated with ROC Curve for better understanding. © 2021 IEEE.

Vehicle Position Tracker Using Simple Global Positioning System that Integrated to Smartphone

Vehicles such as bicycles are in great demand during the current COVID-19 pandemic. Trending topics sometimes follow the needs of the community. The existence of Covid-19 makes a person must maintain the body's immunity. One way to maintain the body's immune system is by cycling. The higher the public demand and the less availability of bicycles in the market, the price of bicycles becomes expensive. The high price of bicycles attracts the attention of thieves to commit bicycle theft. Therefore, the purpose of this paper is to create a bicycle safety system that can be monitored in real time. Making a vehicle position monitor using a simple global positioning system (GPS) integrated with a smartphone. The advantage of this paper is that it uses a simple tracking system so that the price is cheaper and easier to make compared to the existing tracking system. The results obtained are that the location is shown to be quite accurate and can be accessed anywhere via a smartphone. On the smartphone, the software application is installed as a user-friendly media user interface. © 2021 IEEE.