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During the COVID-19 Pandemic, the need for rapid and reliable alternative COVID-19 screening methods have motivated the development of learning networks to screen COVID-19 patients based on chest radiography obtained from Chest X-ray (CXR) and Computed Tomography (CT) imaging. Although the effectiveness of developed models have been documented, their adoption in assisting radiologists suffers mainly due to the failure to implement or present any applicable framework. Therefore in this paper, a robotic framework is proposed to aid radiologists in COVID-19 patient screening. Specifically, Transfer learning is employed to first develop two well-known learning networks (GoogleNet and SqueezeNet) to classify positive and negative COVID-19 patients based on chest radiography obtained from Chest X-Ray (CXR) and CT imaging collected from three publicly available repositories. A test accuracy of 90.90%, sensitivity and specificity of 94.70% and 87.20% were obtained respectively for SqueezeNet and a test accuracy of 96.40%, sensitivity and specificity of 95.50% and 97.40% were obtained respectively for GoogleNet. Consequently, to demonstrate the clinical usability of the model, it is deployed on the Softbank NAO-V6 humanoid robot which is a social robot to serve as an assistive platform for radiologists. The strategy is an end-to-end explainable sorting of X-ray images, particularly for COVID-19 patients. Laboratory-based implementation of the overall framework demonstrates the effectiveness of the proposed platform in aiding radiologists in COVID-19 screening. Author
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Due to the Covid-19 pandemic, two problems arose. Students lacked 1) social opportunities and 2) motivation to maintain their schedules, e.g., studying or relaxing, as their work-life balance disappeared. Thus, we designed a social companion robot, Bulb, that helped students cycle through daily activities with subtle cues, i.e., light, gaze, and movements. Bulb's "head"would light up with different colors or it gazes at different parts of the room, e.g., at the laptop to hint at studying or wiggling to suggest a small break. Five students evaluated Bulb through at-home use, which demonstrated that Bulb was seen as a "living being"and students were responsive to its social cues, like following Bulb's gaze, resulting in a higher awareness and follow-through of students' schedules. Our contribution is in designing a social companion robot that subtly persuaded students through light and anthropomorphic social cues, helping them maintain their daily schedule during the pandemic. © 2023 Owner/Author.
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Accelerated by the COVID-19 pandemic, anthropomorphic service robots are continuously penetrating various domains of our daily lives. With this development, the urge for an interdisciplinary approach to responsibly design human-robot interaction (HRI), with particular attention to human dignity, privacy, compliance, and transparency, increases. This paper contributes to design science, in developing a new artifact, i.e., an interdisciplinary framework for designing responsible HRI with anthropomorphic service robots, which covers the three design science research cycles. Furthermore, we propose a multi-method approach by applying this interdisciplinary framework. Thereby, our finding offer implications for designing HRI in a responsible manner. © 2022 IEEE Computer Society. All rights reserved.
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The use of robotic systems for doctor-patient interaction during Covid-19 and in post-pandemic phases has been proven useful. On the other hand, in current implementations, teleoperating a robot in critical contexts such as the medical scenario may induce a high mental workload on the operator, mainly due to the need to adapt to the remote control of a complex robot, and the reduced environmental awareness. Furthermore, robotic platforms for telemedicine do not usually offer the possibility of establishing physical contact with the patient, which may indeed be useful to show how to assume a certain posture, or to guide a specific movement. The aim of this work is to overcome these limitations, by creating a framework in which the arms, the head, and the base of a humanoid robot can be easily teleoperated with a rapid learning curve and a low mental workload for the operator. The proposed approach is based on the real-time human pose estimation of the operator, which is calculated in real-time and transformed into correspondent skeleton joint angles, used as input to control the upper body joints of the Softbank Robotics robot Pepper. Experiments with users have been performed to check the effectiveness of the imitation system, by verifying the similarity between the human and robot pose and measuring its usability and perceived workload. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Shared autonomous vehicles (SAVs) are revolutionizing the future of urban mobility. This study aims to investigate the effects of artificial intelligence (i.e., autonomy level and anthropomorphic characteristics), human-related, environmental, and societal factors on public trust and acceptance. Structural equation modelling is used to analyze a valid survey sample of 451 participants. Results show that autonomy level can both directly and indirectly (via trust) increase public acceptance;Whereas, anthropomorphic characteristics cannot directly affect public acceptance, but can indirectly increase their acceptance via trust. The other human-related, environmental, and societal factors also positively contribute to public acceptance. Additionally, moderators, including age, gender, income, housing size, COVID-19 history, shared mobility experience, vehicle ownership, and driving experience are also examined. In theory, this study contextualizes the trust-in-automation three-factor model, UTAUT model, and trust theory and includes two domain-specific constructs (i.e., SAV anthropomorphism and SAV autonomy) to study public trust and acceptance towards SAVs. In practice, this study suggests the incorporation of some anthropomorphic features and relatively high autonomy level in SAVs to build public trust and acceptance.
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With the Covid-19 raging around the world, economic and social problems are facing a considerable impact. Many patients who have undertaken rehabilitation treatment in hospitals must be forced to rehabilitate at home. To solve the problems, this paper presents a rehabilitation assistance system combining artificial intelligence and humanoid robots. First, the humanoid robot can play the role of a physical therapist, demonstrating rehabilitation movements and leading patients to rehabilitate. Second, OpenPose model is used to track the human skeleton during rehabilitation. Finally, we compare with the pre-set standard actions, calculate their similarity, and then convert them into quantitative scores for subsequent recording and tracking. The proposed method is verified on the COCO 2017 dataset and self-collected LSVT dataset, and achieves an average precision of 85.5% for skeleton detection. Experiment results from 9 subjects show that the more the non-standard actions are, the lower the score is. This proved that the presented work can provide an effective monitoring method for home rehabilitation. © 2022 IEEE.
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This work shows the development of an open online platform that contains the instructions to build a low-cost humanoid robot. This platform is aimed at all Spanish-speaking educational levels given the lack of information on the development of humanoid robots in this language. The project began at the Tecnologico de Monterrey Campus Morelia with the intention that students of Mechatronics Engineering would replicate an open-source humanoid and thus be able to apply all their knowledge, skills, and competencies in electronics, programming, 3D modeling, and additive manufacturing. Using information from this platform, a robot with a focus on outreach was built that can move its arms and head bidirectionally, as well as its fingers. In addition, a second robot was developed with the aim of acting as a health filter in the Covid-19 pandemic contingency. © 2022 IEEE.
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A service (social) robot is defined as the Internet of Things (IoT) consisting of a physical robot body that connects to one or more Cloud services to facilitate human-machine interaction activities to enhance the functionality of a traditional robot. Many studies found that anthropomorphic designs in robots resulted in greater user engagement. Humanoid service robots usually behave like natural social interaction partners for human users, with emotional features such as speech, gestures, and eye-gaze, referring to the users’ cultural and social background. During the COVID-19 pandemic, service robots play a much more critical role in helping to safeguard people in many countries nowadays. This paper gives an overview of the research issues from technical and social-technical perspectives, especially in Human-Robot Interaction (HRI), emotional expression, and cybersecurity issues, with a case study of gamification and service robots. © 2022, Springer Nature Switzerland AG.
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Responding to these global COVID-19 changes for daily healthcare services clinic, while maintaining safe social distancing, the paper reports the human-centred iterative design with real-fields feasibility inquiries to investigate the first robotic nurse and her partners in Wales. The research adapted the ancient Eastern human nature of seven emotions and six biological wills for the selection criteria and novel design principles for the care robots. We report the preliminary work for integrating, customising, implementing and evaluating three novel robotic nurses: Robot Nightingale, Robot Almeida and Robot Eureka in a care home and a hospital. Bionic Scenarios Definition with 5 merging principles are extracted from the Feasibility Inquiries 1–3. Limitations are discussed from the stakeholders’ experiences. Our research has no intension to replace human nurses, but a thoughtful feasibility and interdisciplinary study for bionic robotic nurses for conventional engineers’ and practitioners’ references. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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In the year 2020, countries were in a race against the spread of Covid-19, leading to major deficiencies in the areas of health, economy, and construction. For this reason, the robotics industry emerged as a viable and safe option to perform important and critical tasks in different sectors, one of them is the real estate. For this reason, a robotic arm was designed to wall painting, this study is supported by the mechatronics engineering department of the Universidad Tecnológica del Perú. The designed robot called: 'UTP-ISR01' has 6 axes and a linear displacement of 2.8 m with turns of 0.24 sec/60°. For the calculation of the forward kinematics the Denavit Hartenberg method was used, then the homogeneous transformation matrices were used to calculate the rotation and translation movements of the robotic manipulator. With the equations identified in the inverse kinematics, the positions and orientations of the robot were plotted, as well as the dimensions of the working area. The CAD design was carried out with engineering software, such as Autodesk Inventor for the mechanical design and assembly of the parts. In addition, with RoboDK software, kinematic simulations and analysis were performed. In conclusion, the robotic arm will reduce the delivery times of the apartments built by the real estate companies. © 2022 IEEE.
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Significant with COVID-19 pandemic breakout, and the high risk of acquiring this infection that is facing the Healthcare Workers (HCWs), a safe alternative was needed. As a result, robotics, artificial intelligence (AI) and internet of things (IoT) usage rose significantly to assist HCWs in their missions. This paper aims to represent a humanoid robot capable of performing HCWs’ repetitive scheduled tasks such as monitoring vital signs, transferring medicine and food, or even connecting the doctor and patient remotely, is an ideal option for reducing direct contact between patients and HCWs, lowering the risk of infection for both parties. Humanoid robots can be employed in a variety of settings in hospitals, including cardiology, post-anesthesia care, and infection control. The creation of a humanoid robot that supports medical personnel by detecting the patient's body temperature and cardiac vital signs automatically and often and autonomously informs the HCWs of any irregularities is described in this study. It accomplishes this objective thanks to its integrated mobile vital signs unit, cloud database, image processing, and Artificial Intelligence (AI) capabilities, which enable it to recognize the patient and his situation, analyze the measured values, and alert the user to any potentially worrisome signals. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The Covid-19 pandemic caused worldwide tragic loss of human life along with social and economic disruptions. The hospitality and tourism sectors were particularly hit hard due to lockdowns for maintaining social distancing to handle the spread of the disease. In this crisis scenario, the adoption of artificial intelligence (AI) and robotics gave massive support to hospitals, hotels, airports, transportation systems, scenery areas and society. Technology played a significant role to reduce human contact and the potential spread of the virus. Humanoid robots, drones, autonomous driving vehicles, and socially intelligent robots performed essential tasks like disinfecting public areas, delivering necessities at people's doorstep, giving safety, measuring body temperature and comforting the patients. This article depicts different roles played by the robots in various sectors during the pandemic and particularly highlights the opportunity that AI and robotics can provide to the hospitality and tourism industry. Tourism and hospitality scholars should develop and adopt robotic applications to foster guests' experiences and protect natural and cultural resources. The use of robotic applications in future can be further used to improve people's collaboration in tourism development. © 2021 IEEE.
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Humanity has always aspired to provide life-like traits and characteristics to its products to discover a replacement for himself to carry out his commands and also to adapt easily in an unfriendly environment. Since the late 2019, covid-19 pandemic has spread all over the world cause immense damage in the healthcare unit worldwide.To make a comeback into normal lifestyle towards medical unit robots are used for better treatment of patients. By considering these features, the authors have designed and developed a Low-Cost 3D printed humanoid robotic arm which can also be controlled and operated wirelessly. The proposed model in this paper uses a wireless RF module which acts as a common interface between the master and slave part of the prototype. The software platform Arduino IDE is used to give instructions to the hardware via high level code. The controller executes the program and performs the necessary movements and functions of the robotic arm in desired manner. The proposed model works on wireless technology which not only makes it more sophisticated at low cost but also provides six degrees of freedom. This model can be a key ingredient to free police workers in hazardous conditions, towards intelligent local traffic management systems and smart cities. © 2021 IEEE.