ABSTRACT
Public restrooms can be a breeding ground for germs and viruses, especially in light of the current COVID-19 pandemic. Touching surfaces like door handles can have a lot of harmful bacteria and microorganisms that increase the risk of transmission of infectious diseases. Additionally, ensuring the cleanliness of public restrooms can be a challenge as its being used by a lot of people on a day-to-day basis. To overcome this, we propose a model that provides a touchless door-locking mechanism with self-sanitization capabilities, thereby reducing the risk of transmission and ensuring a safer and cleaner environment for users. As the Internet of Things is an evolving technology and is providing modern solutions for various problems, the proposed system uses touchless doors that are incorporated with Node Microcontroller Unit and automatic Ultraviolet C sanitization. UVC light radiation is used for disinfecting purposes. The overall invention combines various features to provide a hygienic, secure, and safe restroom experience, ensuring that the restroom is always clean, secure, and accessible to those who need it. © 2023 IEEE.
ABSTRACT
Touch-based fingerprints are widely used in today's world;even with all the success, the touch-based nature of these is a threat, especially in this COVID-19 period. A solution to the same is the introduction of Touchless Fingerprint Technology. The workflow of a touchless system varies vastly from its touch-based counterpart in terms of acquisition, pre-processing, image enhancement, and fingerprint verification. One significant difference is the methods used to segment desired fingerprint regions. This literature focuses on pixel-level classification or semantic segmentation using U-Net, a key yet challenging task. A plethora of semantic segmentation methods have been applied in this field. In this literature, a spectrum of efforts in the field of semantic segmentation using U-Net is investigated along with the components that are integral while training and testing a model, like optimizers, loss functions, and metrics used for evaluation and enumeration of results obtained. © 2022 IEEE.
ABSTRACT
In the modern era of computers, various new technologies have been arising. One such thing is a touchless application that is used or controlled aerially with hand gestures and movements. Augmented reality and virtual reality have come into use which is controlled by gesture controls. Applications that work with gesture controls have started targeting all kinds of users. Python libraries like MediaPipe and OpenCV are used in hand-tracking, palm detection and object detection. Our work aims in developing a virtual painter that helps young children to draw simple images and shapes of varying sizes. The tool recognizes the hand with hand and palm detector models of MediaPipe and capture the modes for selection and drawing using OpenCV library. In the covid pandemic where children are stuck at home and everything has become online, this tool helps them in practicing simple shapes virtually and also makes it interesting for them. The system is tested by drawing aerially with hands and using selection/drawing modes. It worked well with less time latency due to the inbuilt SSD algorithm used in MediaPipe. © 2022 IEEE.
ABSTRACT
Recent advances have introduced IoT as one of the key technologies globally. As safety remains a critical issue for those who spend much time outside. Automated security systems are very useful where safety is an important issue. With a prospect of a Zero User Interface (UI) model this work represents a novel IoT based smart vault security system. The system is built and designed based on IoT combining with Arduino-Uno and Bluetooth module. This system involves LDR sensor, IR sensor and Sonar sensor for monitoring. The vault provides security on three levels. Password protected entry to connect with the smartphone using Bluetooth module, IR sensor array to use 'secret gesture pattern' to unlock the door, tracking number of transactions from the vault using Sonar sensor and LDR was used as a switch. To avoid the replication of physical unlocking of objects IR sensor array was used to introduce 'secret gesture pattern' unlocking system through touchless interfaces for the avoidance of transmissive diseases like COVID-19. This novel system has substantial possibility as a security vault system for industrial and residential use in a contactless manner. © 2023 IEEE.
ABSTRACT
The highly contagious COVID'19 virus's extensive distribution caused the pandemic, which intensified the importance of personal cleanliness and health. Wearing a protective face mask, keeping a certain physical distance, and regularly washing your hands with soap or hand sanitizer are a few precautions you may take to stay safe during this pandemic. An automatic touchless temperature-monitoring doorbell can provide guarded and touch-free temperature sensing, thus informing the household members. The widespread usage of outdated touch-type doorbells may result in the transmission of the coronavirus. The aforementioned article describes a novel approach to creating a Novel Doorbell system that can be activated using gestures and simultaneously detects the person's temperature and notifies the home of a suspected infectious disease. © 2023 IEEE.
ABSTRACT
The Covid-19 pandemic accelerated the need for touchless interactions with technology in public spaces. By leveraging mobile phones which are equipped with Bluetooth wireless transmitters, we enable touchless and app-less interactions between people and technology. The Bluetooth signal broadcast from everyday devices such as smart phones is used by Bluetooth receivers, such as wireless earbuds, to pair the devices and communicate data. In this work, on the other hand, the broadcasted Bluetooth signal is detected, along with any change in the signal caused by rotating one's phone or waving one's hand by the phone. These intentional gestures with one's phone in the presence of an equipped Bluetooth receiver are interpreted as 'words' of a communication language. Without the need to pair devices or download any software, the communication language enables touchless interactions between a user holding a phone and a computing system in a public space © 2023 IEEE.
ABSTRACT
Recently, with the outbreak of the COVID-19 pandemic, various quarantine measures have been implemented to reduce the spread of the virus. As a part of efforts, the preference for touchless technology has been emerging. In this paper, we propose a touchless elevator control system using CNN-based hand gesture recognition. Experimental results show that the hand recognition AP and FPS on the Jetson TX2 board are 81.87% and 11.8FPS, respectively. We demonstrate that an elevator model could be controlled by virtual elevator buttons utilizing CNN-based hand gesture recognition. The proposed method can be applied to commercial elevators as an approach to prevent the spread of viruses from elevator buttons. © 2023 IEEE.
ABSTRACT
Following the global pandemic of COVID-19, in August 2021, Indonesia achieved a total of 3.930.300 cases, the highest in Southeast Asia. However, the government is keen on promoting the new normal phase and planning to open schools and permit face-to-face learning, from elementary up to universities. This means that public facilities and infrastructures will be used and can be the medium for virus transmission, as it will require 48 to 72 hours for the virus to be inactive on those surfaces. This will make people reluctant to touch surfaces, especially when it comes to public facilities that can provide for their needs. One of the most important is the need for hydration which is often overlooked. About 25% of college students were found dehydrated, and 37,5% showed signs of it. Dehydration could prove a serious threat to health had it been overlooked and could affect physical and cognitive performance, having more effects on students and lectures, requiring both in their activities. To support the needs of hydration amidst the pandemic, this research developed a touchless water dispenser system using the waterfall model, utilizing a cloud database with ESP32, controlled by users through an android application. The design is easy and cheap to install, even on regular dispensers, making it an effective and efficient alternative public facility providing hydration service to support the new normal phase © IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License
ABSTRACT
Due to the dread of the pandemic Covid-19 spreading, everyone is staying away from public properties in today's situation. People are even afraid of ringing temple bells, which is a gesture of devotion. Even if they desire to ring the bell, the pandemic prevents them from doing so. The people will be able to ring the temple bell without having to touch it at all owing to this project. The main components employed in this project are an Arduino UNO R3 and ESP8266 boards. An ultrasonic sensor is linked to one ESP8266 board, and it identifies persons when they come within the designated distance range. A relay is attached to the Arduino UNO, which is then connected to a motor. When a person enters the range of the ultrasonic sensor, the ultrasonic sensor sends a message to the relay via the Wi-Fi connection created between the ESP8266 1 and ESP8266 2. As the temple bell is connected to the motor, it will ring automatically after the relay and motor work in sequence. This effort will assist people in fulfilling their religious obligations without constraint. © 2022 IEEE.
ABSTRACT
Touchless technology has garnered significant interest in recent years because of its effectiveness in combating infectious diseases such as the novel coronavirus (COVID-19). The goal of this study was to develop an inexpensive and high-precision touchless technology. A base substrate was coated with a luminescent material that emitted static-electricity-induced luminescence (SEL), and it was applied at high voltage. An inexpensive web camera was used to verify the relationship between the non-contact distance to a needle and the applied-voltage-triggered luminescence. The SEL was emitted at 20-200 mm from the luminescent device upon voltage application, and the web camera detected the SEL position with an accuracy of less than 1 mm. We used this developed touchless technology to demonstrate a highly accurate real-time detection of the position of a human finger based on SEL.
Subject(s)
COVID-19 , Luminescence , Humans , Static Electricity , TechnologyABSTRACT
Managing attendance is a vital task for every institution. Considering the COVID pandemic where many organizations have resorted to online mode of working, it has become imperative to maintain social distancing and digitize various processes. Thus, for maintaining attendance of the students of schools/colleges or employees of a company, a touchless attendance system is required that records the attendance by capturing faces and does not waste time. This one-of-a-kind application uses a client–server model and captures the faces of students/employees through video feeds from mobile phone cameras, and the images are sent to a server, where image processing is used to process the faces. Further, with the help of dlib and the face recognition library, it identifies the faces and records the attendance in the software itself. The processed image is again sent back to the client android application, and the user gets notified about their attendance. Additional functionalities for data analysis and updating data have also been added to the system. Thus, the whole attendance system is an effort to make the attendance activity easy and efficient. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The entire world is currently bustling battling the danger of the innovative coronavirus. Though losses of life are mounting sending stun waves to the open medicinal services authorities and foundations of different types everywhere throughout the world, worries over close to home cleanliness and actions like public isolation are persistently existent specified to break spreading of the disease. Novel and inventive methods for restricting the infection in its path are generally being talked about through technological loops. This is the manner by which a wide range of touchless technological answers for sense the day by day needs of individuals are getting center. In this specific situation, the job of versatile applications and the Internet of Things (IoT) are likewise going to have a significant effect. At this juncture through the extent of this support, we will clarify a portion of the significant techniques with IoT, and versatile applications can assume a positive job in halting the spread of the COVID-19 disease. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The digitalization of human work has been an ever-evolving process. Student's and employee's attendance systems are automated by using fingerprint biometrics. Specifically covid situation has created the need for touchless attendance system. Many institutions have already implemented a face detection-based attendance system. However, the major problem in designing face-recognising biometric applications is the scalability and accuracy in time to differentiate between multiple faces from a single clip/image. This paper used the OpenFace model for face recognition and developed a multi-face recognition model. The Torch and Python deployment module of deep neural network-based face recognition was used, and it was predicated accurately in time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Kiosk machines have gained good popularity among the general public as they are easy to operate and provide a good interactive interface. As a result, multiple users use the kiosk machine throughout the day to find the information they are looking for. Users interact with the kiosk machine by the means of touching its screen or using the buttons. Due to this, it is observed that throughout the day hundreds or even thousands of people end up touching the surface of the kiosk machine. Because of this hygiene cannot be maintained as it is not possible to sanitize the kiosk machine after each use. This has become a serious issue considering the effects that the Covid-19 pandemic had on the world. Multiple people touching the same surface is one of the most common ways through which the virus can spread. To help deal with this problem we have designed a gesture control system using deep learning techniques through which kiosk machines can be operated in a touch-less way. © 2022 IEEE.
ABSTRACT
The demand for contactless biometric authentication has significantly increased during the COVID-19 pandemic and beyond to prevent the spread of Coronavirus. The global pandemic unexpectedly affords a greater opportunity for contactless authentication, but iris and facial recognition biometrics have many usability, security, and privacy challenges, including mask-wearing and presentation attacks (PAs). Mainly, liveness detection against spoofing is notably a challenging task as various biometric authentication methods cannot efficiently assess the real user's physical presence in unsupervised environments. Although several face anti-spoofing methods have been proposed using add-on sensors, dynamic facial texture features, and 3-D mapping, most of them require expensive sensors and substantial computational resources, or fail to detect sophisticated 3-D face spoofing. This article presents a software-based facial liveness detection method named Apple in My Eyes (AIME). AIME is intended to detect the liveness against spoofing for mobile device security using challenge-response testing. AIME generates various screen patterns as authentication challenges, then passively detects corneal-specular reflection responses from human eyes using a frontal camera and analyzes the detected reflections using lightweight machine learning techniques. AIME system components include challenge and pattern detection, feature extraction and classification, and data augmentation and training. We have implemented AIME as a cross-platform application compatible with Android, iOS, and the Web. Our comprehensive experimental results reveal that AIME detects liveness with high accuracy at around 200-ms against different types of sophisticated PAs. AIME can also efficiently detect liveness in multiple contactless biometric authentications without any costly extra sensors nor involving users' active responses.
ABSTRACT
An increasing number of companies move to a touchless user interface, partly because of the pandemic's impact and the restricted rules of using public access objects. The market for Zero User Interface is increasing and industrial implementations are growing and promising. Currently, the knowledge of keeping yourself healthy and clean is essential to prevent the spread of Covid-19 disease. A proposed project - a hand gesture-controlled application to properly guide the user through the handwashing process can increase the overall level of hygiene in public. The solution is realized with a transfer learning method based on EfficientNet Lite models making it possible to run on Android, iOS, embedded Linux devices, and microcontrollers. © 2022 IEEE.
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Due to the current labor shortage situation, combined with the spread of COVID-19, the researchers came up with the idea of developing a contactless remote robotic arm system based on IoT. This research focuses on developing prototypes of remote control three-axis robotic arm via the Internet that can be applied in industrial, medical, and other applications. Abiding by the new normal situation, the Kinect sensor control input, a device capable of receiving commands from human gestures without touching, is used to alleviate the spread of the virus. From the development and experiment, it can be shown that the developed artifact can receive commands from human gestures to remotely control the robotic arm via the Internet in accordance with the intended purpose. © 2021 IEEE.
ABSTRACT
Two years on with Covid-19, touchless technology has evolved from a device that symbolizes luxury to something that is necessary. Eye tracker is one type of touchless technologies that uses user's gaze to interact with computer without touching the screen. Development of spontaneous gazebased interaction is progressing very rapidly. Researchers have developed various object selection methods without prior gazeto-screen calibration. Recently, the conventional approach of setting threshold was developed as a gaze-based object selection method. However, the use of threshold values is considered non-adaptive and requires additional data pre-processing to handle noises. To overcome this problem, deep learning is used as an object selection method for spontaneous gaze-based interaction. Deep learning does not require any data preprocessing method to achieve accurate object selection results. Out of five deep learning algorithms that were evaluated, LSTM (Long Short-Term Memory) and BiLSTM (Bidirectional Long Short-Term Memory) networks achieved comparable accuracy of 95.17 pm 0.95% and 95.15 pm 1.17%, respectively. In future, our research is promising for development of real-time object selection technique for touchless public display. © 2022 IEEE.
ABSTRACT
Gaze-based interaction has until now been almost an exclusive prerogative of the assistive field, as it is considered not sufficiently performing compared to traditional communication methods based on keyboards, pointing devices, and touch screens. However, situations such as the one we are experiencing now due to the COVID-19 pandemic highlight the importance of touchless communication, to minimize the spread of the disease. In this paper, as an example of the potential pervasive use of eye tracking technology in public contexts, we propose and study five interfaces for a gaze-controlled scale, to be used in supermarkets to weigh fruits and vegetables. Given the great heterogeneity of potential users, the interaction must be as simple and intuitive as possible and occur without the need for calibration. The experiments carried out confirm that this goal is achievable and show strengths and weaknesses of the five interfaces. © 2022 ACM.
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There is an increasing interest in creating interactive learning applications using innovative interaction technologies, especially in STEM (Science, technology, engineering, and mathematics) subjects. Recent developments in machine learning have allowed for nearly perfect hand-tracking recognition, introducing a touchless modality for interaction within Augmented Reality(AR) environments. However, the research community has not explored the pedagogical approach of Kinesthetic Learning or “Learning by Doing”, hand tracking, and machine learning agents combined with Augmented Reality technology. Fundamentally, this exploration of touchless interaction technologies has taken on new importance in the new post-COVID world. Meanwhile, machine learning has gained attention for its ability to enhance personalized learning and play a vital new role as a virtual instructor. This paper proposes a novel approach called the AGILEST approach, which uses machine learning Agents to facilitate interactive kinesthetic learning in STEM education through touchless interaction. The first case study for this approach will be an AR learning application for chemistry. This application uses real-time touchless hand interaction for kinesthetic learning and uses a machine learning agent to act as both trainer and assessor of the user. The evaluation of this research has been conducted remotely through a usability study with expert reviewers, which includes 15 young researchers with peer-reviewed work in Human-Computer Interaction & AR and 2 subject experts STEM teachers at the secondary school level. The usability evaluation through NASA Task Load Index (NASA-TLX), Perceived Ease of Use(PUEU), and Perceived Usefulness(PU) with expert reviewers provide positive feedback about this approach for productive learning gain, engagement and interactiveness in learning STEM subjects.