Path planning research for mobile robots in dynamic environments

At present, due to the COVID-19, China's social and economic development has slowed down. Some life service e-commerce platforms have successively launched "contactless delivery" services, which can effectively curb the spread of the epidemic. Robot distribution is the current mainstream, but robots are different from people and need to have accurate program settings. Both path planning and obstacle avoidance are currently top issues. This requires the mobile robot to successfully arrive at the destination while minimizing the impact on the surrounding environment and pedestrians, and avoiding encroachment on the movement space of pedestrians. Therefore, the mobile robot needs to be able to actively avoid moving pedestrians in a dynamic environment, in addition to avoiding static obstacles, and safely and efficiently integrate into the pedestrian movement environment. In this paper, the path planning problem of unmanned delivery robot is studied, and the path of mobile robot in the crowd is determined by global planning and local planning, and the matlab simulation is used for verification. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.

Face Recognition Attendance System

The conventional procedure used in all of India's major regions is attendance monitoring on paper with pens. Although the final data is computerized, it takes a long time to get from a classroom to a database. The effectiveness of the classes is directly impacted by the number of absences. The attendance takes up almost half of the lecture's allotted time. The alternative method that is being used involves using fingerprints, but even this approach is ineffective since it takes so long. Due to the illnesses (COVID-19) spreading over the world, however, the situation as it stands right now does not make this the best course of action. Therefore, it will be advisable to develop a contactless and more efficient. © 2023 IEEE.

Automated Contactless Continuous Temperature Monitoring System for Pandemic Disease Controlling Infrastructures

People are being thermally screened in hospitals and in such facilities, all the data collected must be stored and displayed. The person responsible for keeping track of people's body temperatures must put in more time and effort. This approach is a tedious task, especially during times of dealing with the pandemic diseases like Covid-19. Hence, in this paper, an automated contactless continuous temperature monitoring system is designed to eliminate this time-consuming process. If a person's temperature is too high, that is, higher than the usual temperature range, the system records it and monitors it continuously via a mobile application. In this paper, we present the development of an Automated contactless continuous body temperature monitoring system using a Raspberry Pi camera and mobile application. © 2023 IEEE.

COVID-19 Vaccine and Booster Shots Compatibility Analyzer using CMOS-based Monochrome Encoding

The continuous increase in COVID-19 positive cases in the Philippines might further weaken the local healthcare system. As such, an efficient system must be implemented to further improve the immunization efforts of the country. In this paper, a contactless digital electronic device is proposed to assess the vaccine and booster brand compatibility. Using Logisim 2.7.1, the logic diagrams were designed and simulated with the help of truth tables and Boolean functions. Moreover, the finalized logic circuit design was converted into its equivalent complementary metal-oxide semiconductor (CMOS) and stick diagrams to help contextualize how the integrated circuits will be designed. Results have shown that the proposed device was able to accept three inputs of the top three COVID-19 vaccine brands (Sinovac, AstraZeneca, and Pfizer) and assess the compatibility of heterologous vaccinations. With the successful results of the circuit, it can be concluded that this low-power device can be used to manufacture a physical prototype for use in booster vaccination sites. © 2022 IEEE.

Automated Contactless Attendance System

Attendance marking in a classroom is a tedious and time-consuming task. Due to a large number of students present, there is always a possibility of proxy. In recent times, the task of automatic attendance marking has been extensively addressed via the use of fingerprint-based biometric systems, radio frequency identification tags, etc. However, these RFID systems lack the factor of dependability and due to COVID-19 use of fingerprint-based systems is not advisable. Instead of using these conventional methods, this paper presents an automated contactless attendance system that employs facial recognition to record student attendance and a gesture sensor to activate the camera when needed, thereby consuming minimal power. The resultant data is subsequently stored in Google Spreadsheets, and the reports can be viewed on the webpage. Thus, this work intends to make the attendance marking process contactless, efficient and simple. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Optimization of Contactless Distribution in the Post-COVID Era

During an emergency, timely and effective distribution of emergency supplies is critical in rescue. In the context of Covid-19, given the difficulties in distributing supplies to communities due to super infectious viruses, unmanned vehicle distribution is studied by taking into account the priority and satisfaction of communities to improve distribution safety and effectiveness of supplies. Furthermore, the influence of distribution time on the overall efficiency is also taken into account, thus ultimately establishing an unmanned distribution model with the shortest distribution time while meeting community satisfaction. The improved whale algorithm is used to solve the dual-objective model and compared with the basic whale optimization algorithm. The results show that the improved whale algorithm demonstrates better convergence, searchability, and stability. The constructed model can scientifically distribute daily necessities to communities while considering their priority and satisfaction. © 2022 IEEE.

Cardless Cash Withdrawal Using Palm Vein Technology

Generally, the easiest way to withdraw money from your bank account is by using an Automated Teller Machine (ATM). The user can withdraw the money by inserting their card into the slot on the machine, and then entering a four-digit Personal Identification Number (PIN) to complete the transaction process. Similarly, some banks adopted the method of using a One Time Password (OTP) to complete the transaction process to make it more secure. With the recent advancements in technology, there are many new methods that can be used for withdrawing money from ATMs, like cardless cash withdrawal or using one's biometrics. But, due to the recent COVID-19 pandemic, we refrain from using things that are not sanitized properly. People started avoiding going to the ATMs since hygiene was a major concern during the pandemic. Also, due to the constant hand washing and the use of sanitizers, the use of conventional biometrics was not efficient. As a result, the idea of using a method that is contact-less and is also more secure emerged, i.e., the palm vein technology. The palm vein technology uses a person's vein pattern, which is unique to everyone and can help us achieve better results with greater accuracy. The paper proposes a concept of using a person's vein pattern as a method of contact-less authentication. It is an extremely safe verification procedure because no two people in the world, not even identical twins, can have the same palm vein structure or pattern. Additionally, it is more secure because it is nearly impossible to replicate the palm vein pattern. © 2022 IEEE.

COVID-19 and Viral Pneumonia Classification Using Radiomic Features and Deep Learning

The new coronavirus has become the greatest challenge of the 21st century. But since the first cases, much is being discovered about the disease and its effects on the body. Medical imaging, such as X-Rays and CT is widely used to visualize and follow up the patient's clinical picture, especially the effects on the lungs. Although useful, the analysis of this type of image requires some expertise from the radiologist. In less developed countries, the amount of radiologists specialized in chest X-Rays is inadequate, which motivates the development of new technologies to assist clinicians to provide reliable diagnoses. Therefore, this paper addresses the development of a computer-based method to assist in COVID-19 detection among viral pneumonia and health patients through X-Rays images. The proposed method is based on extracting radiomic features and analyzing them using Deep Neural Networks. Experiments following K-Fold Cross-Validation achieved an overall accuracy of 94.98%, a sensibility of 94.89% and an AUC of 99.20%. A benchmark with traditional machine learning algorithms and a binary assessment are also provided. From a multiclass perspective, the analysis and differentiation of COVID-19 and other viral pneumonia reached great results and may assist radiologists in better diagnosing the disease worldwide. © 2022 IEEE.

Multiscale Approach in Deep Convolutional Networks for Minutia Extraction from Contactless Fingerprint Images

Biometric identification by contactless fingerprinting has been a trend in recent years, reinforced by the pandemic of the new coronavirus (COVID-19). Contactless acquisition tends to be a more hygienic acquisition category with greater user acceptance because it is less invasive and does not require the use of a surface touched by other people as traditional acquisition does. However, this area presents some challenging tasks. Contact-based sensors still generally provide greater biometric effectiveness since the minutiae are more pronounced due to the high contrast between ridges and valleys. On the other hand, contactless images typically have low contrast, so the methods fail with spurious or undetectable details, demonstrating the need for further studies in this area. In this work, we propose and analyze a robust scaled deep learning model for extracting minutiae in contactless fingerprint images. The results, evaluated on three datasets, show that the proposed method is competitive against other minutia extraction algorithms and commercial software. © 2022 IEEE.

A Hand Gesture-Based Contact-less Interface for Electronic Health Records

Clinical data monitoring and storing are essential components of continuous and preventive healthcare systems. Data such as blood pressure, pulse rate, temperature, etc., can be recorded by the hospital staff daily for in-patient subjects. The usual way of noting them down is to check different parameters using various medical instruments and write it on paper with the corresponding patient's details (e.g., name, patient-id, or government identity card number). However, after the outbreak of COVID-19, there is a set of World Health Organization (WHO) guidelines to behave in public places. Ordinary people and professionals feel hesitant to touch any media even if they have some protection such as gloves and sanitizer. In this crisis, there is a natural demand for contact-less activities instead of touch-based traditional ways. Gesture-based activities might be one of the low-cost alternatives to some sensor-based systems. This paper uses a profound learning-based finger point gesture to capture writing in the air and realize it on the screen through a predictive model. Here, the proposed framework has been demonstrated as a proof of concept to record blood pressure data for multiple patients without touching any electronic screen or paper. The proposed architecture is developed based on the gesture recognition and metric learning, which have been used to recognize different digits trained from the MNIST digit dataset. The mean test accuracy is reached 99.47% on the same dataset. © 2022 IEEE.

Face Mask and Body Temperature Scanning System for Covid-19

Coronavirus illness 2019 has had a major impact on the entire world over the past two to three years. One important approach for people's protection is to wear masks in public. Furthermore, putting on a mask properly Many public service providers demand that users only utilise the service while properly wearing masks. Only a small number of studies have examined face mask identification using image analysis, nevertheless. We suggest Face Mask, a highly accurate and practical face mask detector, in this study. The suggested Face Mask is a one-stage detector that combines a novel context attention module for detecting face masks with a feature pyramid network to fuse high-level semantic information with various feature maps. We also provide a brand-new cross-class object removal method to reject and predictions with a high intersection of union and low confidence. Additionally, we investigate the viability of integrating Face Mask with a portable or embedded neural network called MobileNet. By utilising1)Contactless temperature sensing,2)we create a fack mask detection alarm system to boost COVID-19 indoor safety.Infrared sensor and contactless temperature sensing subsystems rely on Arduino Uno, while computer vision algorithms are used for mask identification. © 2023 IEEE.

Non-contact sensing system based on GaN optoelectronic chip

As the global spread of COVID-19 becomes a rapidly evolving crisis, the development of contactless shared interactive displays is an urgent issue to reduce the risk of viral and bacterial cross contamination due to the use of touch-operated shared user terminals. Here, we experimentally demonstrate a contactless user terminal fabricated with a monolithic GaN Optoelectronic system (MGOS), which integrates the transmitter and receiver into a single chip. The inherent spectral emission-responsiveness overlap of GaN QW diodes gives the device a unique ability to detect light transmitted by diodes that share the same QW structure. When the GaN transmitter emits light to illuminate an external object, the integrated GaN receiver can detect the reflected light encoding the information and convert the optical signal into an electrical signal, so that the non-contact user terminal has the ability to use light for bidirectional data communication. Compared to traditional handwriting systems, these terminals operate as contactless information entry devices that can help reduce potential cross-contamination due to contact with handwriting terminals, provide precautions to keep the environment clean, and help prevent virus transmission. © 2023 IEEE.

AI Enabled Smart Campus for Health Safety and Monitoring

COVID 19 has had a major effect on society. In order to keep people's spacing, new requirements have been placed in place regarding the amount of users authorized in individual rooms in offices, shops, etc. Along with social distance, regular temperature verification at mall entrances are indeed permitted. An excellent embedded machine learning system is proposed in this work to identify face masks automatically and detect the body's temperature in a real-time application. The proposed system, in particular, utilizes a raspberry pi camera to capture real-time video simultaneously by identifying face masks with the help of a classification technique. The face mask detector is constructed by utilizing mobilenetv2 and imaging net pre-trained weights to consider three scenarios: wearing a mask correctly, wearing a mask incorrectly, and not wearing any at all. By placing a temperature gauge on a Raspberry Pi, a framework has also been developed for determining a person's body temperature. The numerical outcomes show the feasibility and performance of our integrated devices in compared to many cutting-edge research. This temperature and facemask detection device monitors a person's body heat and detects whether or not that person is wearing a facemask. Consequently, any organization's entrance could contain this device. In this study, the door is only released if the temperature is below 99° F, which would be calculated by the Electro Selective Pattern-32 images, the MLX sensor, and the fact that a person's face is 80% protected by a facemask. © 2023 IEEE.

IoT-enabled Contactless Doorbell with Facial Recognition

The COVID-19 epidemic has altered lifestyles all across the globe, causing people to take additional safety precautions and make using a face mask a requirement. Face masks are becoming more popular, making it occasionally challenging for people to recognize other people. Children and the elderly in particular would have trouble identifying their masked guests, which poses a serious hazard because thieves or burglars would take advantage of the situation. In this study, a system was created using IoT and deep learning technologies that works as a unit to offer a contactless solution to the ongoing COVID-19 pandemic while also enabling home owners to keep track of their visitors and receive notifications when someone comes over. The contactless doorbell was created with the help of a Raspberry Pi and a modified ResNet-50 model using ArcFace loss as the feature extractor to efficiently extract visible features from a masked face and support very accurate recognition. Due to the lack of a real masked face dataset with sufficient data, this study used a data augmentation method to add masks to face images from a dataset. The model was able to achieve a recognition accuracy of 98.27% when evaluated using a masked LFW dataset. Furthermore, testing the face recognition model in real-Time with limited users, each with and without a mask yielded an accuracy of 100% in unmasked facial recognition and 90% on masked facial recognition. © 2023 IEEE.

Applications of Mobile Payment Services in Financial Strategies: State-of-the-Art, Taxonomy and Upcoming Directions with a Focus on Pandemic Crisis

Amid the pandemic infection, people are bound to use contactless mobile payment (M-Payment) services. M-Payment is a payment method using an application in a mobile device, such as a mobile phone, and gadget. Owing to the convenience, reliability and contact-free feature of M-Payment, it has been diffusely adopted to reduce the direct and indirect contacts in transactions, allowing social distancing to be maintained and facilitating the stabilization of the social economy. Consequently, it has become one of the day's most important topics. Therefore, the purpose of this study is to provide a systematic literature review (SLR) on the applications of M-payment services in financial strategies, focusing on the pandemic crisis. 19 papers were collected and divided into three groups for further analysis. The results showed that M-Payments applications in financial strategies during the pandemic crisis could help reduce the spread of infection risks by hastening the transition to touchless habits. © 2023 World Scientific Publishing Company.

Real-time Face Mask Detection and Contactless Body Temperature Measurement on Edge Device Using Deep Learning

The world is severely affected by COVID-19 disease and it has became a threat to everyone. One of the effective methods to prevent infection of this disease is to wear a face mask in public places. The body temperature of a person is an important indicator of COVID-19 infection. Many public places or services give entry to the people only if they wear a mask and have body temperature in a normal range. In areas like college labs, internet cafes and malls, they keep a daily log of visiting persons with details such as name, date, body temperature etc. In this work, a system is proposed that can be utilized to remind people to wear a face mask and monitor them. It can also measure body temperature using an IR temperature sensor and alert respective authorities if it is high. In the proposed system, convolutional neural network MobileNetV2 is used for face mask detection deployed on NVIDIA Jetson Nano. © 2022 IEEE.

When Contactless Service Matters: The Use of Robotic Services in the Healthcare Sector

The Coronavirus disease 2019 (COVID-19) pandemic has led to a catastrophic public health emergency that impacted the global society's prosperity, health, and security. Concurrently, the swift technological development over the recent decades has enabled the rising implementation of robots in various industries. In particular, there is a growing demand for robotic technology in the healthcare sector as a precautionary measure since they significantly reduce the risk of cross-infection through interpersonal contact among medical professionals by shifting to computerized routine tasks. Therefore, this paper presents a comprehensive review of the use of robots in the healthcare sector during and post-COVID-19 pandemic. This paper highlights the increasing demand and adoption of robotic technology during and post-pandemic COVID-19 in healthcare sector. The benefits to the society and engineering managers, and challenges in implementing robotic technology in the healthcare sector are provided at the end of this paper before the paper ends with a concise conclusion. IEEE

Contactless Fog based Handwash Kit for COVID Safety

Recent days have changed tremendously, and rules are strictly being deployed to maintain social distancing, avoid crowding and frequent hand washing. Frequent washing of hands using our domestic water by a mass crowd result in water wastage which is a huge loss for our society. A better solution to sanitize the hands with reduced water wastage is attempted in this study. With technological advancements in engineering, several solutions and cope-up methods are being given to combat the spread of COVID-19 in this pandemic era. As an attempt, this study develops a Fog based Contactless Handwash kit which uses the Mis Spray method to sanitize the hands. The mist consists of water vapour and herbal sanitizer which is skin-friendly to humans. This kit is suggested to be deployed in public places to avoid the spreading of the virus since it is in a complete contactless manner. It is developed with an Atmega based microcontroller, NodeMCu,ultrasonic sensor and mist spray module economically. The outcomes of the developed handwash kit serve to optimally favour the preventing behaviour in this pandemic time. This study gives way for further research studies on the automatic sanitizing methods to combat the spread of the virus and its variants. © 2022 IEEE.

Implementation of PRESENT Algorithm on Contactless Access Control using Raspberry Pi

This research develops a contactless and secure access control system based on face recognition and body temperature measurement. This research aims to establish a security system that also fulfills health protocols for COVID-19 spreading, in this case, the limitation of physical contact. The PRESENT algorithm, a lightweight block cipher encryption-decryption algorithm, is implemented to keep the transmitted data safe. The face recognition method consists of the Viola-Jones face detection algorithm and LBPH face recognition algorithm. The body temperature is measured using a contactless sensor. The performance tests show the accuracies of recognizing faces are 68% under 198 Lux lighting and 52% under 105 Lux lighting. The precision of measuring body temperature using the sensor reaches 98,85%. Based on the sniffing attack test of the system, the encrypted data transmitted from the system to the web-based database is safe from attackers. Besides the face spoofing attack tests, the system will not authenticate attackers with face photos or face videos. © 2022 IEEE.

Smart Attendance System using Dlib Pre-Trained Neural Network

Recognizing and remembering various people is the most frequent task, which the human brain performs. With regard to this, the process of attendance becomes one of the hectic tasks, which requires subsequent modernization. The spread of COVID- 19 is also drastically increasing and are pushed to the situation of wearing mask the entire time. This brings in a situation of misidentifying the individuals and are also prone to impersonation in many official gatherings such as exams, meetings, etc. This cannot be decreased by unmasking their face in this pandemic situation just for the purpose of verification as it may lead to increase in COVID risk. Here, this research study implements a contactless face recognition system with a simple and smart database, which can take in any form of data as per the convenience. This system solves the above problem by making the face recognition smart using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) classifier. The main task of the system is to recognize the user's face (live) and automatically mark the time of recognition directly in the Google sheet along with the alphabets of P(Present), A(absent) or L(late) according to the given time range. This system makes effective use of google sheet for easy share ability, accessibility, and error free management. This can be used for number of purposes such as exam centers, schools, colleges, companies, hospitals and various other places in order to verify the people (contact less). © 2023 IEEE.