Robots Under COVID-19 Pandemic: A Comprehensive Survey

Efficacy and Safety of a Medical Robot for Non-Face-to-Face Nasopharyngeal Swab Specimen Collection: Nonclinical and Clinical Trial Findings for COVID-19 Testing

ObjectivesTo meet the high demand for polymerase chain reaction (PCR) tests to diagnose COVID-19 and rapidly control the outbreak, an efficient and safe molecular diagnostic protocol is necessary. In this study, we evaluated the efficacy and safety of the medical robot developed for non-face-to-face nasopharyngeal swab specimen collection.MethodsIn a nonclinical study, an otorhinolaryngologist collected swab specimens manually and using a medical robot. In a single-institution, randomized, open-label, prospective, exploratory clinical trial, nasopharyngeal swab specimens were collected from the enrolled participants both manually and by using the medical robot.ResultsEvaluation of the efficacy and safety of nasopharyngeal swab collection using a medical robot was assessed. After the operation of the robot, subjective discomfort experienced by the participants and any side effects or abnormalities in the nose were also monitored. Preliminary nonclinical data revealed comparable results between robotic and manual methods in terms of RNA metrics and cytokeratin-8 expression. Minor initial damage to A549 cells by the robot improved with subsequent use. In the clinical setting, the robot-assisted technique yielded a 92.31% detection rate for human RNase P, while the manual method achieved 100%. Post-swabbing discomfort reported by participants was similar for both methods and resolved within 48 h.ConclusionsThe medical robot system could efficiently, safely, and accurately collect nasopharyngeal swab samples in a non-face-to-face manner. Its installation in respiratory clinics, airports, or ports could minimize the infection risk between individuals and healthcare workers, thereby contributing to an efficient distribution of medical resources.

Development of a Cable-Driven Bionic Spherical Joint for a Robot Wrist

Wrist movements play a crucial role in upper-limb motor tasks. As prosthetic and robotic hand technologies have evolved, increasing attention has been focused on replicating the anatomy and functionality of the wrist. Closely imitating the biomechanics and movement mechanisms of human limbs is expected to enhance the overall performance of bionic robotic hands. This study presents the design of a tendon-driven bionic spherical robot wrist, utilizing two pairs of cables that mimic antagonist muscle pairs. The cables are actuated by pulleys driven by servo motors, allowing for two primary wrist motions: flexion-extension and ulnar-radial deviation. The performance Please confirm if the "1583 Iiyama" is necessary. Same as belowof the proposed robot wrist is validated through manipulation experiments using a prototype, demonstrating its capability to achieve a full range of motion for both ulnar and radial deviation. This wrist mechanism is expected to be integrated into robotic systems, enabling greater flexibility and more human-like movement capabilities.

Mobile robots for isolation-room hospital settings: A scenario-based preliminary study

Isolated patients pose physical challenges to medical staff owing to the need for protective gear. Additionally, communication issues arise within isolation rooms, hampering patient care. Mobile robots offer potential solutions, allowing for contactless communication and efficient task delegation, thereby reducing the risk of cross-contamination and minimizing staff workload. This preliminary study assessed the usability, acceptability, and potential for improvement of mobile robots in clinical nursing scenarios, focusing on nurses' perspectives. A preliminary test was conducted using mobile robots in a simulated hospital environment with 30 experienced nurses responsible for isolated patient care. Data were collected through interviews, surveys, and scenario-based tasks. Two scenarios were designed to evaluate the usability and effectiveness of mobile robots in real-world nursing situations. Nurses regarded mobile robots as highly usable and useful in healthcare settings. Robots efficiently handled tasks like remote supply delivery and medication distribution. Nurses recognized the potential for improved communication and efficiency with mobile robots; however, concerns were raised about the robots' limitations in providing emotional support and potential safety issues during emergencies. This research emphasizes the promising role of mobile robots in enhancing healthcare delivery within isolation rooms. While these findings indicate the potential for mobile robots, careful planning, training, and scenario development are crucial for their safe and effective integration into clinical settings. Further research, tailored scenarios, and a reevaluation of the evolving role of nurses in a technology-augmented healthcare environment are necessary, emphasizing the importance of understanding the capabilities and limitations of robotic assistance in patient care.

Advancing healthcare through mobile collaboration: a survey of intelligent nursing robots research

Mobile collaborative intelligent nursing robots have gained significant attention in the healthcare sector as an innovative solution to address the challenges posed by the increasing aging population and limited medical resources. This article provides a comprehensive overview of the research advancements in this field, covering hospital care, home older adults care, and rehabilitation assistance. In hospital settings, these robots assist healthcare professionals in tasks such as patient monitoring, medication management, and bedside care. For home older adults care, they enhance the older adults sense of security and quality of life by offering daily life support and monitoring. In rehabilitation, these robots provide services such as physical rehabilitation training and social interaction to facilitate patient recovery. However, the development of intelligent nursing robots faces challenges in technology, ethics, law, and user acceptance. Future efforts should focus on improving robots' perceptual and cognitive abilities, enhancing human-robot interaction, and conducting extensive clinical experiments for broader applications.

Telerobotic Intergroup Contact: Acceptance and Preferences in Israel and Palestine

We explore telerobotics as a novel form of intergroup communication. In this form, remotely operated robots facilitate embodied and situated intergroup contact between groups in conflict over long distances, potentially reducing prejudice and promoting positive social change. Based on previous conceptual frameworks and design hypotheses, we conducted a survey on the acceptance and preferences of the telerobotic medium in Israel and Palestine. We analyzed the responses using a mixed-method approach. The results shed light on differences in attitudes between the groups and design considerations for telerobots when used for intergroup contact. This study serves as a foundation for the implementation of a novel method of technology-enhanced conflict resolution in the field.

COVID-19 Detection Using Contemporary Biosensors and Machine Learning Approach: A Review

The current global pandemic not only claims countless human lives but also rocks the economies of every country on the planet. This fact needs the development of novel, productive, and efficient techniques to detect the SARS-CoV-2 virus. This review article discusses the current state of SARS-CoV-2 virus detection methods such as electrochemical, fluorescent, and electronic, etc., as well as the potential of optical sensors with a wide range of novel approaches and models. This review provides a comprehensive comparison of various detection methods by comparing the various techniques in depth. In addition, there is a brief discussion of the futuristic approach combining optical sensors with machine learning algorithms. It is believed that this study would prove to be critical for the scientific community to explore solutions for detecting viruses with improved functionality.

Propensity to trust shapes perceptions of comforting touch between trustworthy human and robot partners

Touching a friend to comfort or be comforted is a common prosocial behaviour, firmly based in mutual trust. Emphasising the interactive nature of trust and touch, we suggest that vulnerability, reciprocity and individual differences shape trust and perceptions of touch. We further investigate whether these elements also apply to companion robots. Participants (n = 152) were exposed to four comics depicting human-human or human-robot exchanges. Across conditions, one character was sad, the other initiated touch to comfort them, and the touchee reciprocated the touch. Participants first rated trustworthiness of a certain character (human or robot in a vulnerable or comforting role), then evaluated the two touch phases (initiation and reciprocity) in terms of interaction realism, touch appropriateness and pleasantness, affective state (valence and arousal) attributed to the characters. Results support an interactive account of trust and touch, with humans being equally trustworthy when comforting or showing vulnerability, and reciprocity of touch buffering sadness. Although these phenomena seem unique to humans, propensity to trust technology reduces the gap between how humans and robots are perceived. Two distinct trust systems emerge: one for human interactions and another for social technologies, both necessitating trust as a fundamental prerequisite for meaningful physical contact.

Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects

The impact of the coronavirus disease 2019 (COVID-19) pandemic on the everyday livelihood of people has been monumental and unparalleled. Although the pandemic has vastly affected the global healthcare system, it has also been a platform to promote and develop pioneering applications based on autonomic artificial intelligence (AI) technology with therapeutic significance in combating the pandemic. Artificial intelligence has successfully demonstrated that it can reduce the probability of human-to-human infectivity of the virus through evaluation, analysis, and triangulation of existing data on the infectivity and spread of the virus. This review talks about the applications and significance of modern robotic and automated systems that may assist in spreading a pandemic. In addition, this study discusses intelligent wearable devices and how they could be helpful throughout the COVID-19 pandemic.

Risks of Drone Use in Light of Literature Studies

This article aims to present the results of a bibliometric analysis of relevant literature and discuss the main research streams related to the topic of risks in drone applications. The methodology of the conducted research consisted of five procedural steps, including the planning of the research, conducting a systematic review of the literature, proposing a classification framework corresponding to contemporary research trends related to the risk of drone applications, and compiling the characteristics of the publications assigned to each of the highlighted thematic groups. This systematic literature review used the PRISMA method. A total of 257 documents comprising articles and conference proceedings were analysed. On this basis, eight thematic categories related to the use of drones and the risks associated with their operation were distinguished. Due to the high content within two of these categories, a further division into subcategories was proposed to illustrate the research topics better. The conducted investigation made it possible to identify the current research trends related to the risk of drone use and pointed out the existing research gaps, both in the area of risk assessment methodology and in its application areas. The results obtained from the analysis can provide interesting material for both industry and academia.

Human-Robot Collaboration for Healthcare: A Narrative Review

Robotic applications have often quickly transitioned from industrial to social. Because of this, robots can now engage with people in a natural way and blend in with their surroundings. Due to the lack of medical professionals, growing healthcare costs, and the exponential rise in the population of vulnerable groups like the ill, elderly, and children with developmental disabilities, the use of social robots in the healthcare system is expanding. As a result, social robots are employed in the medical field to entertain and educate hospitalized patients about health issues, as well as to assist the elderly and sick. They are also employed in the dispensing of medications, rehabilitation, and emotional and geriatric care. Thus, social robots raise the standard and effectiveness of medical care. This article explains how patients and healthcare professionals collaborate with robots in the healthcare industry. The objectives of this collaboration are to resolve moral and legal concerns, improve patient outcomes, and improve healthcare delivery. It has a broad range of uses, including telemedicine, rehabilitation, and robotic surgical support. Human-robot interaction is the term used to describe interactions between social robots and people. Many obstacles stand in the way of human-robot interaction in healthcare, including safety concerns, acceptability issues, appropriateness, usefulness, and the worry that robots may replace human carers. In the end, these difficulties result in a poor adoption rate for robotic technology. As a result, the applications and difficulties of human-robot interaction in healthcare are thoroughly evaluated in this research. This study also reviews future safety prospects from human-robot interaction in healthcare, as well as ethical and usability issues including privacy, trust, and safety, and our aims to provide a comprehensive overview of the use of robots in healthcare, including their applications, benefits, challenges, and prospects, to facilitate a deeper understanding of this evolving field.

Telehealth interventions during COVID-19 pandemic: a scoping review of applications, challenges, privacy and security issues

BACKGROUND

The COVID-19, caused by the SARS-CoV-2 virus, proliferated worldwide, leading to a pandemic. Many governmental and non-governmental organisations and research institutes are contributing to the COVID-19 fight to control the pandemic.

MOTIVATION

Numerous telehealth applications have been proposed and adopted during the pandemic to combat the spread of the disease. To this end, powerful tools such as artificial intelligence (AI)/robotic technologies, tracking, monitoring, consultation apps and other telehealth interventions have been extensively used. However, there are several issues and challenges that are currently facing this technology.

OBJECTIVE

The purpose of this scoping review is to analyse the primary goal of these techniques; document their contribution to tackling COVID-19; identify and categorise their main challenges and future direction in fighting against the COVID-19 or future pandemic outbreaks.

METHODS

Four digital libraries (ACM, IEEE, Scopus and Google Scholar) were searched to identify relevant sources. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was used as a guideline procedure to develop a comprehensive scoping review. General telehealth features were extracted from the studies reviewed and analysed in the context of the intervention type, technology used, contributions, challenges, issues and limitations.

RESULTS

A collection of 27 studies were analysed. The reported telehealth interventions were classified into two main categories: AI-based and non-AI-based interventions; their main contributions to tackling COVID-19 are in the aspects of disease detection and diagnosis, pathogenesis and virology, vaccine and drug development, transmission and epidemic predictions, online patient consultation, tracing, and observation; 28 telehealth intervention challenges/issues have been reported and categorised into technical (14), non-technical (10), and privacy, and policy issues (4). The most critical technical challenges are: network issues, system reliability issues, performance, accuracy and compatibility issues. Moreover, the most critical non-technical issues are: the skills required, hardware/software cost, inability to entirely replace physical treatment and people's uncertainty about using the technology. Stringent laws/regulations, ethical issues are some of the policy and privacy issues affecting the development of the telehealth interventions reported in the literature.

CONCLUSION

This study provides medical and scientific scholars with a comprehensive overview of telehealth technologies' current and future applications in the fight against COVID-19 to motivate researchers to continue to maximise the benefits of these techniques in the fight against pandemics. Lastly, we recommend that the identified challenges, privacy, and security issues and solutions be considered when designing and developing future telehealth applications.

How can social robot use cases in healthcare be pushed - with an interoperable programming interface

INTRODUCTION

Research into current robot middleware has revealed that most of them are either too complicated or outdated. These facts have motivated the development of a new middleware to meet the requirements of usability by non-experts. The proposed middleware is based on Android and is intended to be placed over existing robot SDKs and middleware. It runs on the android tablet of the Cruzr robot. Various toolings have been developed, such as a web component to control the robot via a webinterface, which facilitates its use.

METHODS

The middleware was developed using Android Java and runs on the Cruzr tablet as an app. It features a WebSocket server that interfaces with the robot and allows control via Python or other WebSocket-compatible languages. The speech interface utilizes Google Cloud Voice text-to-speech and speech-to-text services. The interface was implemented in Python, allowing for easy integration with existing robotics development workflows, and a web interface was developed for direct control of the robot via the web.

RESULTS

The new robot middleware was created and deployed on a Cruzr robot, relying on the WebSocket API and featuring a Python implementation. It supports various robot functions, such as text-to-speech, speech-to-text, navigation, displaying content and scanning bar codes. The system's architecture allows for porting the interface to other robots and platforms, showcasing its adaptability. It has been demonstrated that the middleware can be run on a Pepper robot, although not all functions have been implemented yet. The middleware was utilized to implement healthcare use cases and received good feedback.

CONCLUSION

Cloud and local speech services were discussed in regard to the middleware's needs, to run without having to change any code on other robots. An outlook on how the programming interface can further be simplified by using natural text to code generators has been/is given. For other researchers using the aforementioned platforms (Cruzr, Pepper), the new middleware can be utilized for testing human-robot interaction. It can be used in a teaching setting, as well as be adapted to other robots using the same interface and philosophy regarding simple methods.

The digital world and atypical work: Perceptions and difficulties of teleworking in Hungary and Romania

IT solutions have been transforming the world of work, albeit with varying intensity, for decades. They affect, among other things, the organisation of work, work structures, employment patterns and working time. Solutions such as artificial intelligence (AI), business intelligence (BI), robotic process automation (RPA), workflow, process mining, etc. are now a daily reality in the corporate sector, be it physical or mental work. However, the spread of digital solutions across organisations, spaces and sectors is uneven. Can the impact of this uneven spread be observed in employment, especially in atypical forms of employment? In our questionnaire research, we seek to establish how employees with tertiary education in two pairs of sectors (Hungarian and Romanian non-profit sectors and Hungarian non-profit and for-profit sectors) in two countries perceive some demographic characteristics of teleworking. In our research, we analysed the responses of Hungarian and Romanian working-age citizens with tertiary education who had been working in telework for at least one year at the time of completing the questionnaire. In addition to the deterioration of work relationships and the lack of personal contacts already shown by many studies, it is observed that the work-life balance is upset for single people and those without children, while the number of children does not influence opinions on teleworking. The differences observed and presented in the study are typically not gender-specific but largely based on globalised perspectives.

Care Robotics: An Assessment of Professional Perception in the Face of the COVID-19 Pandemic

The COVID-19 crisis accelerated the adoption of technologies. Technological advancement is also expected in robotics applied to any sector, including in healthcare. The aim is to assess the professional perception of care robotics facing COVID-19. This study aimed to (1) select a tool for assessing different aspects of healthcare, (2) analyse the professional perception about the development, usefulness and helpfulness of technologies and robotics in the field of healthcare and (3) evaluate the correlation between the perceived helpfulness of care robotics and the selected tool. We implement five validated clinical tests which integrate 80 items about a person and their clinical situation. From the sample of 46 professionals, 95.65% affirmed that technology was moderately to completely useful for professional performance in the context of the pandemic, lowering to 67.39% when asked only about robotics; 93.48% stated that the inclusion of robotics in at least one health area affected by COVID-19 would have helped them. Finally, the variables extracted from clinical tests corresponded to the most relevant health areas as identified by the professionals. This research shows the potential of care robotics oriented towards healthcare from a care paradigm.

A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning

This article proposes a novel method for detecting coronavirus disease 2019 (COVID-19) in an underground channel using visible light communication (VLC) and machine learning (ML). We present mathematical models of COVID-19 Deoxyribose Nucleic Acid (DNA) gene transfer in regular square constellations using a CSK/QAM-based VLC system. ML algorithms are used to classify the bands present in each electrophoresis sample according to whether the band corresponds to a positive, negative, or ladder sample during the search for the optimal model. Complexity studies reveal that the square constellation N=22i×22i,(i=3) yields a greater profit. Performance studies indicate that, for BER = 10-3, there are gains of -10 [dB], -3 [dB], 3 [dB], and 5 [dB] for N=22i×22i,(i=0,1,2,3), respectively. Based on a total of 630 COVID-19 samples, the best model is shown to be XGBoots, which demonstrated an accuracy of 96.03%, greater than that of the other models, and a recall of 99% for positive values.

ROMI: A Real-Time Optical Digit Recognition Embedded System for Monitoring Patients in Intensive Care Units

With advances in the Internet of Things, patients in intensive care units are constantly monitored to expedite emergencies. Due to the COVID-19 pandemic, non-face-to-face monitoring has been required for the safety of patients and medical staff. A control center monitors the vital signs of patients in ICUs. However, some medical devices, such as ventilators and infusion pumps, operate in a standalone fashion without communication capabilities, requiring medical staff to check them manually. One promising solution is to use a robotic system with a camera. We propose a real-time optical digit recognition embedded system called ROMI. ROMI is a mobile robot that monitors patients by recognizing digits displayed on LCD screens of medical devices in real time. ROMI consists of three main functions for recognizing digits: digit localization, digit classification, and digit annotation. We developed ROMI by using Matlab Simulink, and the maximum digit recognition performance was 0.989 mAP on alexnet. The developed system was deployed on NVIDIA GPU embedded platforms: Jetson Nano, Jetson Xavier NX, and Jetson AGX Xavier. We also created a benchmark by evaluating the runtime performance by considering ten pre-trained CNN models and three NVIDIA GPU platforms. We expect that ROMI will support medical staff with non-face-to-face monitoring in ICUs, enabling more effective and prompt patient care.

A Self-Collision Detection Algorithm of a Dual-Manipulator System Based on GJK and Deep Learning

Self-collision detection is fundamental to the safe operation of multi-manipulator systems, especially when cooperating in highly dynamic working environments. Existing methods still face the problem that detection efficiency and accuracy cannot be achieved at the same time. In this paper, we introduce artificial intelligence technology into the control system. Based on the Gilbert-Johnson-Keerthi (GJK) algorithm, we generated a dataset and trained a deep neural network (DLNet) to improve the detection efficiency. By combining DLNet and the GJK algorithm, we propose a two-level self-collision detection algorithm (DLGJK algorithm) to solve real-time self-collision detection problems in a dual-manipulator system with fast-continuous and high-precision properties. First, the proposed algorithm uses DLNet to determine whether the current working state of the system has a risk of self-collision; since most of the working states in a system workspace do not have a self-collision risk, DLNet can effectively reduce the number of unnecessary detections and improve the detection efficiency. Then, for the working states with a risk of self-collision, we modeled precise colliders and applied the GJK algorithm for fine self-collision detection, which achieved detection accuracy. The experimental results showed that compared to that with the global use of the GJK algorithm for self-collision detection, the DLGJK algorithm can reduce the time expectation of a single detection in a system workspace by 97.7%. In the path planning of the manipulators, it could effectively reduce the number of unnecessary detections, improve the detection efficiency, and reduce system overhead. The proposed algorithm also has good scalability for a multi-manipulator system that can be split into dual-manipulator systems.

Visualization in Anatomy Education

In the post-pandemic era, one of the significant challenges for anatomy teachers is to reciprocate the experience of practical exposure while teaching the subject to undergraduates. These challenges span from conducting cadaveric dissections to handling real human bones, museum specimens, and tissue sections in the histology lab. Such exposures help the instructors to develop interactive communication with their fellow students and thus help to enhance communication skills among them. Recently, anatomy teachers all over the world started using cutting-edge educational technologies to make teaching-learning experiences for students more engaging, interesting, and interactive. Utilizing such cutting-edge educational technologies was an "option" prior to the pandemic, but the pandemic has significantly altered the situation. What was previously an "option" is now a "compulsion." Despite the fact that the majority of medical schools have resumed their regular on-campus classes, body donation and the availability of cadavers remain extremely limited, resulting in a deadlock. Anatomy teachers must incorporate cutting-edge educational technologies into their teaching and learning activities to make the subject more visual. In this chapter, we have attempted to discuss various new technologies which can provide a near-realistic perception of anatomical structures as a complementary tool for dissection/cadaver, various visualization techniques currently available and explore their importance as a pedagogic alternative in learning anatomy. We also discussed the recent advancement in visualization techniques and the pros and cons of technology-based visualization. This chapter identifies the limitations of technology-based visualization as a supplement and discusses effective utilization as an adjunct to the conventional pedagogical approaches to undergraduate anatomy education.

Evolution from Medical Imaging to Visualized Medicine

The discovery of X-ray in 1895 and the first X-ray image of Mrs. Röntgen's hand opened up a new era of radiology and the research of medical imaging. The evolution of traditional medical imaging has been lasting for over 100 years, serving the detection, diagnosis, and treatments of human diseases with a clear view of the anatomy information. In late 1990s, the concept of molecular imaging was proposed as the science and technology of molecular biology and bio-engineering rapidly developed, and it directly gave birth to the emergence of precision medicine for clinical lesion-targeted treatments against various cancers and cardiocerebrovascular diseases. Physiological and pathological changes in live bodies from zebrafish to human beings can be imaged to ensure an efficient image-guided therapy. Nowadays, the philosophy of medical and molecular imaging has been a powerful tool and indispensable modality for doctors to make their decisions and give patients reliable advices. With the ever-emerging developments of advanced intelligent technologies such as flexible sensors, medical meta-data analysis, brain sciences, surgical robots, VR/AR, etc., modern medicine has been evolving from traditional medical and molecular imaging to visualized medicine, which has created novel accessible approaches along with cutting-edge techniques for the revolutionized diagnostic and therapeutic paradigms. In this context, the history and milestones from medical imaging to visualized medicine will be elucidated. And in particular, representative visualized medicine advances including its application to COVID-19 epidemics will be discussed in order to look for its important contributions and a future perspective to modern medicine.

A Systematic Review on Social Robots in Public Spaces: Threat Landscape and Attack Surface

There is a growing interest in using social robots in public spaces for indoor and outdoor applications. The threat landscape is an important research area being investigated and debated by various stakeholders. Objectives: This study aims to identify and synthesize empirical research on the complete threat landscape of social robots in public spaces. Specifically, this paper identifies the potential threat actors, their motives for attacks, vulnerabilities, attack vectors, potential impacts of attacks, possible attack scenarios, and mitigations to these threats. Methods: This systematic literature review follows the guidelines by Kitchenham and Charters. The search was conducted in five digital databases, and 1469 studies were retrieved. This study analyzed 21 studies that satisfied the selection criteria. Results: Main findings reveal four threat categories: cybersecurity, social, physical, and public space. Conclusion: This study completely grasped the complexity of the transdisciplinary problem of social robot security and privacy while accommodating the diversity of stakeholders’ perspectives. Findings give researchers and other stakeholders a comprehensive view by highlighting current developments and new research directions in this field. This study also proposed a taxonomy for threat actors and the threat landscape of social robots in public spaces.

Robot-Assisted Rehabilitation Architecture Supported by a Distributed Data Acquisition System

Rehabilitation robotics aims to facilitate the rehabilitation procedure for patients and physical therapists. This field has a relatively long history dating back to the 1990s; however, their implementation and the standardisation of their application in the medical field does not follow the same pace, mainly due to their complexity of reproduction and the need for their approval by the authorities. This paper aims to describe architecture that can be applied to industrial robots and promote their application in healthcare ecosystems. The control of the robotic arm is performed using the software called SmartHealth, offering a 2 Degree of Autonomy (DOA). Data are gathered through electromyography (EMG) and force sensors at a frequency of 45 Hz. It also proves the capabilities of such small robots in performing such medical procedures. Four exercises focused on shoulder rehabilitation (passive, restricted active-assisted, free active-assisted and Activities of Daily Living (ADL)) were carried out and confirmed the viability of the proposed architecture and the potential of small robots (i.e., the UR3) in rehabilitation procedure accomplishment. This robot can perform the majority of the default exercises in addition to ADLs but, nevertheless, their limits were also uncovered, mainly due to their limited Range of Motion (ROM) and cost.

Performance analysis of autonomous UV disinfecting robot (UV bot) using Taguchi method

As a result of the COVID-19 epidemic, there is a growing demand for robots to perform various operations which include service bots, cleaning, and disinfection bots. Viral contamination has been one of the major causes of human fatality which has abruptly increased in this situation. Availability of existing technologies is always surpassed by an effective one so as is the UV-Bot developed in this project. This bot aims for a highly accurate percentage of up to 96.8% of germ clearance at pre-defined conditions which are user-friendly. Also, the robot is designed in a compact size and effective shape to achieve maximum efficiency. The robot is deployed in hospital pathway and rooms for disinfection whereas human detection and obstacle avoidance has been included with a custom-developed algorithm that supports autonomous navigation and corner tracking facility. The robot also supports live streaming of the disinfecting site with an emergency alarm and stop in human detection. This type of robot is highly capable of destroying viral infections at a particular point which is validated using Taguchi analysis and also the robot is 3D modelled and tested using static and dynamic obstacles. Thus UV-Bot is manually controllable or autonomous which uses the A* algorithm to store or retrieve the disinfecting site map which is recorded if used frequently.

Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead

Artificial Intelligence (AI) has played a substantial role in the response to the challenges posed by the current pandemic. The growing interest in using AI to handle Covid-19 issues has accelerated the pace of AI research and resulted in an exponential increase in articles and review studies within a very short period of time. Hence, it is becoming challenging to explore the large corpus of academic publications dedicated to the global health crisis. Even with the presence of systematic review studies, given their number and diversity, identifying trends and research avenues beyond the pandemic should be an arduous task. We conclude therefore that after the one-year mark of the declaration of Covid-19 as a pandemic, the accumulated scientific contribution lacks two fundamental aspects: Knowledge synthesis and Future projections. In contribution to fill this void, this paper is a (i) synthesis study and (ii) foresight exercise. The synthesis study aims to provide the scholars a consolidation of findings and a knowledge synthesis through a systematic review of the reviews (umbrella review) studying AI applications against Covid-19. Following the PRISMA guidelines, we systematically searched PubMed, Scopus, and other preprint sources from 1st December 2019 to 1st June 2021 for eligible reviews. The literature search and screening process resulted in 45 included reviews. Our findings reveal patterns, relationships, and trends in the AI research community response to the pandemic. We found that in the space of few months, the research objectives of the literature have developed rapidly from identifying potential AI applications to evaluating current uses of intelligent systems. Only few reviews have adopted the meta-analysis as a study design. Moreover, a clear dominance of the medical theme and the DNN methods has been observed in the reported AI applications. Based on its constructive systematic umbrella review, this work conducts a foresight exercise that tries to envision the post-Covid-19 research landscape of the AI field. We see seven key themes of research that may be an outcome of the present crisis and which advocate a more sustainable and responsible form of intelligent systems. We set accordingly a post-pandemic research agenda articulated around these seven drivers. The results of this study can be useful for the AI research community to obtain a holistic view of the current literature and to help prioritize research needs as we are heading toward the new normal.

Application of 5G network combined with AI robots in personalized nursing in China: A literature review

The medical and healthcare industry is currently developing into digitization. Attributed to the rapid development of advanced technologies such as the 5G network, cloud computing, artificial intelligence (AI), and big data, and their wide applications in the medical industry, the medical model is shifting into an intelligent one. By combining the 5G network with cloud healthcare platforms and AI, nursing robots can effectively improve the overall medical efficacy. Meanwhile, patients can enjoy personalized medical services, the supply and the sharing of medical and healthcare services are promoted, and the digital transformation of the healthcare industry is accelerated. In this paper, the application and practice of 5G network technology in the medical industry are introduced, including telecare, 5G first-aid remote medical service, and remote robot applications. Also, by combining application characteristics of AI and development requirements of smart healthcare, the overall planning, intelligence, and personalization of the 5G network in the medical industry, as well as opportunities and challenges of its application in the field of nursing are discussed. This paper provides references to the development and application of 5G network technology in the field of medical service.

Liaison, safeguard, and well-being: Analyzing the role of social robots during the COVID-19 pandemic

We examine the implementation of social robots in real-world settings during the COVID-19 pandemic. In particular, we analyze the areas in which social robots are being adopted, the roles and tasks being fulfilled, and the robot models being implemented. For this, we traced back and analyzed 240 deployment cases with 86 different social robots worldwide that have been adopted since the coronavirus outbreak. We found that social robot adoption during this period was strongly related to the use of this technology for crisis management. The social robots' capacity to perform the roles of liaison to minimize direct contact among humans, safeguard to ensure contagion risk-free environments, and well-being coach to protect mental and physical health, is key to explaining adoption within this context. The results of the study offer a complete overview of social robots' utilization in real life settings during the pandemic.

Liaison, safeguard, and well-being: Analyzing the role of social robots during the COVID-19 pandemic

We examine the implementation of social robots in real-world settings during the COVID-19 pandemic. In particular, we analyze the areas in which social robots are being adopted, the roles and tasks being fulfilled, and the robot models being implemented. For this, we traced back and analyzed 240 deployment cases with 86 different social robots worldwide that have been adopted since the coronavirus outbreak. We found that social robot adoption during this period was strongly related to the use of this technology for crisis management. The social robots' capacity to perform the roles of liaison to minimize direct contact among humans, safeguard to ensure contagion risk-free environments, and well-being coach to protect mental and physical health, is key to explaining adoption within this context. The results of the study offer a complete overview of social robots' utilization in real life settings during the pandemic.

A Better Integration of Industrial Robots in Romanian Enterprises and the Labour Market

The purpose of this paper is to illustrate the opportunities for adopting robotic applications, through a marketing mix perspective, as well as depicting the current state of industrial robot integration in Romanian enterprises and the labor market, in contrast to other economies of the European Union. In this research, we highlight the impact of industrial robots within enterprises, while also considering the perceived standard of living through GDP per capita. For this, we conducted exploratory research based on secondary data regarding the evolution of the robotics sector in Romania, in connection to the dynamics of the global and European Union robotics market. We also performed a principal components analysis, which revealed the main factors that contributed to the dynamics of nation-level enterprise statistics. Our analysis revealed that a higher integration of industrial robots contributed to the reduction of employment rates amongst all six EU countries considered, while also having positive correlations with the GDP per capita and apparent labor productivity. Mixed results were only observed for the impact of industrial robots on remuneration growth, suggesting the potential adverse effects automation could have on incomes.

All-printed soft human-machine interface for robotic physicochemical sensing

Ultrasensitive multimodal physicochemical sensing for autonomous robotic decision-making has numerous applications in agriculture, security, environmental protection, and public health. Previously reported robotic sensing technologies have primarily focused on monitoring physical parameters such as pressure and temperature. Integrating chemical sensors for autonomous dry-phase analyte detection on a robotic platform is rather extremely challenging and substantially underdeveloped. Here, we introduce an artificial intelligence-powered multimodal robotic sensing system (M-Bot) with an all-printed mass-producible soft electronic skin-based human-machine interface. A scalable inkjet printing technology with custom-developed nanomaterial inks was used to manufacture flexible physicochemical sensor arrays for electrophysiology recording, tactile perception, and robotic sensing of a wide range of hazardous materials including nitroaromatic explosives, pesticides, nerve agents, and infectious pathogens such as SARS-CoV-2. The M-Bot decodes the surface electromyography signals collected from the human body through machine learning algorithms for remote robotic control and can perform in situ threat compound detection in extreme or contaminated environments with user-interactive tactile and threat alarm feedback. The printed electronic skin-based robotic sensing technology can be further generalized and applied to other remote sensing platforms. Such diversity was validated on an intelligent multimodal robotic boat platform that can efficiently track the source of trace amounts of hazardous compounds through autonomous and intelligent decision-making algorithms. This fully printed human-machine interactive multimodal sensing technology could play a crucial role in designing future intelligent robotic systems and can be easily reconfigured toward numerous practical wearable and robotic applications.

Control Matters in Elder Care Technology:: Evidence and Direction for Designing It In

Studies find that older adults want control over how technologies are used in their care, but how it can be operationalized through design remains to be clarified. We present findings from a large survey (n=825) of a well-characterized U.S. online cohort that provides actionable evidence of the importance of designing for control over monitoring technologies. This uniquely large, age-diverse sample allows us to compare needs across age and other characteristics with insights about future users and current older adults (n=496 >64), including those concerned about their own memory loss (n=201). All five control options, which are not currently enabled, were very or extremely important to most people across age. Findings indicate that comfort with a range of care technologies is contingent on having privacy- and other control-enabling options. We discuss opportunities for design to meet these user needs that demand course correction through attentive, creative work.

Mixed-Reality-Enhanced Human–Robot Interaction with an Imitation-Based Mapping Approach for Intuitive Teleoperation of a Robotic Arm-Hand System

This paper presents an integrated mapping of motion and visualization scheme based on a Mixed Reality (MR) subspace approach for the intuitive and immersive telemanipulation of robotic arm-hand systems. The effectiveness of different control-feedback methods for the teleoperation system is validated and compared. The robotic arm-hand system consists of a 6 Degrees-of-Freedom (DOF) industrial manipulator and a low-cost 2-finger gripper, which can be manipulated in a natural manner by novice users physically distant from the working site. By incorporating MR technology, the user is fully immersed in a virtual operating space augmented by real-time 3D visual feedback from the robot working site. Imitation-based velocity-centric motion mapping is implemented via the MR subspace to accurately track operator hand movements for robot motion control and enables spatial velocity-based control of the robot Tool Center Point (TCP). The user control space and robot working space are overlaid through the MR subspace, and the local user and a digital twin of the remote robot share the same environment in the MR subspace. The MR-based motion and visualization mapping scheme for telerobotics is compared to conventional 2D Baseline and MR tele-control paradigms over two tabletop object manipulation experiments. A user survey of 24 participants was conducted to demonstrate the effectiveness and performance enhancements enabled by the proposed system. The MR-subspace-integrated 3D mapping of motion and visualization scheme reduced the aggregate task completion time by 48% compared to the 2D Baseline module and 29%, compared to the MR SpaceMouse module. The perceived workload decreased by 32% and 22%, compared to the 2D Baseline and MR SpaceMouse approaches.

Analysis of the COVID-19 pandemic’s impacts on manufacturing: a systematic literature review and future research agenda

The COVID-19 pandemic has affected manufacturing companies and necessitated adaptations of firms’ operations. Despite the increasing interest in this subject, a scarcity of systematic analysis can be observed. The present study systematically reviews the existing research on the COVID-19 pandemic concerning the manufacturing industry. This paper aims to highlight the main impacts of the COVID-19 pandemic on the manufacturing sector from the operations management perspective, the practical adaptation actions, and future research opportunities. Open research questions and directions for further investigation are articulated and triangulated across organisational, process and technology perspectives.

Assessment of Pain Onset and Maximum Bearable Pain Thresholds in Physical Contact Situations

With the development of robot technology, robot utilization is expanding in industrial fields and everyday life. To employ robots in various fields wherein humans and robots share the same space, human safety must be guaranteed in the event of a human–robot collision. Therefore, criteria and limitations of safety need to be defined and well clarified. In this study, we induced mechanical pain in humans through quasi-static contact by an algometric device (at 29 parts of the human body). A manual apparatus was developed to induce and monitor a force and pressure. Forty healthy men participated voluntarily in the study. Physical quantities were classified based on pain onset and maximum bearable pain. The overall results derived from the trials pertained to the subjective concept of pain, which led to considerable inter-individual variation in the onset and threshold of pain. Based on the results, a quasi-static contact pain evaluation method was established, and biomechanical safety limitations on forces and pressures were formulated. The pain threshold attributed to quasi-static contact can serve as a safety standard for the robots employed.

Ensuring the business continuity of production companies in conditions of COVID-19 pandemic in Poland - Applied measures analysis

The purpose of the paper is to present the results of the study of solutions implemented in Polish manufacturing companies to ensure their business continuity under COVID-19 pandemic conditions. A questionnaire survey was conducted using the mixed-model CAWI/CATI (Computer Assisted Web Interview/Computer Assisted Telephone Interview) on a representative group of 600 manufacturing companies in Poland. The research was conducted in March 2021, more than a year after the first lockdown. The study included cases of companies in most industries and companies of different sizes. The subject of the survey was to identify legal, technical, and organisational protection measures implemented in manufacturing enterprises in response to the COVID-19 pandemic in manufacturing companies in Poland. The COVID-19 pandemic has forced companies to look for solutions to work safely. Some of the identified work and production organisation solutions are innovative and can serve as models of good practice for other companies. They can also be the basis for learning lessons and preparing for future critical incidents. It is a great economic and social importance to ensure the continuity of the activities of manufacturing enterprises during the pandemic.

Verifiable Surface Disinfection Using Ultraviolet Light with a Mobile Manipulation Robot

Robots are being increasingly used in the fight against highly-infectious diseases such as the Novel Coronavirus (SARS-CoV-2). By using robots in place of human health care workers in disinfection tasks, we can reduce the exposure of these workers to the virus and, as a result, often dramatically reduce their risk of infection. Since healthcare workers are often disproportionately affected by large-scale infectious disease outbreaks, this risk reduction can profoundly affect our ability to fight these outbreaks. Many robots currently available for disinfection, however, are little more than mobile platforms for ultraviolet lights, do not allow fine-grained control over how the disinfection is performed, and do not allow verification that it was done as the human supervisor intended. In this paper, we present a semi-autonomous system, originally designed for the disinfection of surfaces in the context of Ebola Virus Disease (EVD) that allows a human supervisor to direct an autonomous robot to disinfect contaminated surfaces to a desired level, and to subsequently verify that this disinfection has taken place. We describe the overall system, the user interface, how our calibration and modeling allows for reliable disinfection, and offer directions for future work to address open space disinfection tasks.

Human robot interaction as a service for combatting COVID-19: an experimental case study

COVID-19 pandemic has changed today's routines in a variety of fields such as society, economics, health, etc. It is surely known that the most powerful weapon to fight against the disease is the social distancing. Hence, it is strongly recommended by the authorities to decrease human to human interaction (HHI) in order to stop the spread. However, daily routine of people must continue somehow, because of the fact that it is not known when the pandemic will end permanently. Thus, new approaches should be adapted in social environments for COVID-19 prevention. Human robot interaction (HRI) can be seen as a vital mechanism to provide risk free routines in the society. For this purpose, we offer a human robot interaction as a service (HRIaaS) for eatery locations such as restaurants, cafes, etc. where customers should interact with the staff. The proposed service aims to utilize personal smartphones and decrease the number of HHIs for such environments in which strange people involved. Moreover, an experimental case study is conducted to obtain an evaluation with a real world scenario when the proposed service is used versus a contemporary routine with HHIs. The evaluation results show that an average reduction of 41% is achieved per customer in the number of HHIs between customers and serving staff.

Enhanced Robots as Tools for Assisting Agricultural Engineering Students’ Development

Inevitably, the rapid growth of the electronics industry and the wide availability of tailored programming tools and support are accelerating the digital transformation of the agricultural sector. The latter transformation seems to foster the hopes for tackling the depletion and degradation of natural resources and increasing productivity in order to cover the needs of Earth’s continuously growing population. Consequently, people getting involved with modern agriculture, from farmers to students, should become familiar with and be able to use and improve the innovative systems making the scene. At this point, the contribution of the STEM educational practices in demystifying new areas, especially in primary and secondary education, is remarkable and thus welcome, but things become quite uncertain when trying to discover efficient practices for higher education, and students of agricultural engineering are not an exception. Indeed, university students are not all newcomers to STEM and ask for real-world experiences that better prepare them for their professional careers. Trying to bridge the gap, this work highlights good practices during the various implementation stages of electric robotic ground vehicles that can serve realistic agricultural tasks. Several innovative parts, such as credit card-sized systems, AI-capable modules, smartphones, GPS, solar panels, and network transceivers are properly combined with electromechanical components and recycled materials to deliver technically and educationally meaningful results.

Healing Hands: The Tactile Internet in Future Tele-Healthcare

In the early 2020s, the coronavirus pandemic brought the notion of remotely connected care to the general population across the globe. Oftentimes, the timely provisioning of access to and the implementation of affordable care are drivers behind tele-healthcare initiatives. Tele-healthcare has already garnered significant momentum in research and implementations in the years preceding the worldwide challenge of 2020, supported by the emerging capabilities of communication networks. The Tactile Internet (TI) with human-in-the-loop is one of those developments, leading to the democratization of skills and expertise that will significantly impact the long-term developments of the provisioning of care. However, significant challenges remain that require today’s communication networks to adapt to support the ultra-low latency required. The resulting latency challenge necessitates trans-disciplinary research efforts combining psychophysiological as well as technological solutions to achieve one millisecond and below round-trip times. The objective of this paper is to provide an overview of the benefits enabled by solving this network latency reduction challenge by employing state-of-the-art Time-Sensitive Networking (TSN) devices in a testbed, realizing the service differentiation required for the multi-modal human-machine interface. With completely new types of services and use cases resulting from the TI, we describe the potential impacts on remote surgery and remote rehabilitation as examples, with a focus on the future of tele-healthcare in rural settings.

An analysis of international use of robots for COVID-19

This article analyses data collected from press reports, social media, and the scientific literature on 338 instances of robots used explicitly in response to COVID-19 from 24 Jan, 2020, to 23 Jan, 2021, in 48 countries. The analysis was guided by four overarching questions: (1) What were robots used for in the COVID-19 response? (2) When were they used? (3) How did different countries innovate? and 4) Did having a national policy on robotics influence a country's innovation and insertion of robotics for COVID-19? The findings indicate that robots were used for six different sociotechnical work domains and 29 discrete use cases. When robots were used varied greatly on the country; although many countries did report an increase at the beginning of their first surge. To understand the findings of how innovation occurred, the data was examined through the lens of the technology's maturity according to NASA's Technical Readiness Assessment metrics. Through this lens, findings note that existing robots were used for more than 78% of the instances; slightly modified robots made up 10%; and truly novel robots or novel use cases constituted 12% of the instances. The findings clearly indicate that countries with a national robotics initiative were more likely to use robotics more often and for broader purposes. Finally, the dataset and analysis produces a broad set of implications that warrant further study and investigation. The results from this analysis are expected to be of value to the robotics and robotics policy community in preparing robots for rapid insertion into future disasters.

Area-Coverage Planning for Spray-based Surface Disinfection with a Mobile Manipulator

The use of robots has significantly increased to fight highly contagious diseases like SARS-COV-2, Ebola, MERS, and others. One of the important applications of robots to fight such infectious diseases is disinfection. Manual disinfection can be a time-consuming, risky, labor-intensive, and mundane, and humans may fail to disinfect critical areas due to the resulting fatigue. Autonomous or semi-autonomous mobile manipulators mounted with a spray nozzle at the end-effector can be very effective in spraying disinfectant liquid for deep disinfection of objects and surfaces. In this paper, we present an area-coverage planning algorithm to compute a path that the nozzle follows to disinfect surfaces represented by their point clouds. We project the point cloud on a plane and produce a polygon on which we generate multiple spray paths using our branch and bound-based tree search area-coverage algorithm such that the spray paths cover the entire area of the polygon. An appropriate spray path is chosen using a robot capability map-based selection criterion. We generate mobile manipulator trajectories using successive refinement-based parametric optimization so that the paths for the nozzle are followed accurately. Thereafter, we need to make sure that the joint velocities of the mobile manipulator are regulated appropriately such that each point on the surface receives enough disinfectant spray. To this end, we compute the time intervals between the robot path waypoints such that enough disinfectant liquid is sprayed on all points of the point cloud that results in thorough disinfection of the surface, and the particular robot path is executed in the minimum possible time. We have implemented the area-coverage planning and mobile manipulator motion planning on five test scenarios in simulation using our ADAMMS-SD (Agile Dexterous Autonomous Mobile Manipulation System for Surface Disinfection) robot. We benchmark our spray path generation algorithm with three competing methods by showing that the generated paths are significantly more efficient in terms of area coverage and reducing disinfectant wastage. We also show the time interval computation between successive waypoints results in thorough disinfection of surfaces.

Occupant-centric robotic air filtration and planning for classrooms for Safer school reopening amid respiratory pandemics

Coexisting with the current COVID-19 pandemic is a global reality that comes with unique challenges impacting daily interactions, business, and facility maintenance. A monumental challenge accompanied is continuous and effective disinfection of shared spaces, such as office/school buildings, elevators, classrooms, and cafeterias. Although ultraviolet light and chemical sprays are routines for indoor disinfection, they irritate humans, hence can only be used when the facility is unoccupied. Stationary air filtration systems, while being irritation-free and commonly available, fail to protect all occupants due to limitations in air circulation and diffusion. Hence, we present a novel collaborative robot (cobot) disinfection system equipped with a Bernoulli Air Filtration Module, with a design that minimizes disturbance to the surrounding airflow and maneuverability among occupants for maximum coverage. The influence of robotic air filtration on dosage at neighbors of a coughing source is analyzed with derivations from a Computational Fluid Dynamics (CFD) simulation. Based on the analysis, the novel occupant-centric online rerouting algorithm decides the path of the robot. The rerouting ensures effective air filtration that minimizes the risk of occupants under their detected layout. The proposed system was tested on a 2 × 3 seating grid (empty seats allowed) in a classroom, and the worst-case dosage for all occupants was chosen as the metric. The system reduced the worst-case dosage among all occupants by 26% and 19% compared to a stationary air filtration system with the same flow rate, and a robotic air filtration system that traverses all the seats but without occupant-centric planning of its path, respectively. Hence, we validated the effectiveness of the proposed robotic air filtration system.

Physical Distancing Device with Edge Computing for COVID-19 (PADDIE-C19)

The most effective methods of preventing COVID-19 infection include maintaining physical distancing and wearing a face mask while in close contact with people in public places. However, densely populated areas have a greater incidence of COVID-19 dissemination, which is caused by people who do not comply with standard operating procedures (SOPs). This paper presents a prototype called PADDIE-C19 (Physical Distancing Device with Edge Computing for COVID-19) to implement the physical distancing monitoring based on a low-cost edge computing device. The PADDIE-C19 provides real-time results and responses, as well as notifications and warnings to anyone who violates the 1-m physical distance rule. In addition, PADDIE-C19 includes temperature screening using an MLX90614 thermometer and ultrasonic sensors to restrict the number of people on specified premises. The Neural Network Processor (KPU) in Grove Artificial Intelligence Hardware Attached on Top (AI HAT), an edge computing unit, is used to accelerate the neural network model on person detection and achieve up to 18 frames per second (FPS). The results show that the accuracy of person detection with Grove AI HAT could achieve 74.65% and the average absolute error between measured and actual physical distance is 8.95 cm. Furthermore, the accuracy of the MLX90614 thermometer is guaranteed to have less than 0.5 °C value difference from the more common Fluke 59 thermometer. Experimental results also proved that when cloud computing is compared to edge computing, the Grove AI HAT achieves the average performance of 18 FPS for a person detector (kmodel) with an average 56 ms execution time in different networks, regardless of the network connection type or speed.

Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review

The outbreak of the COVID-19 pandemic is unarguably the biggest catastrophe of the 21st century, probably the most significant global crisis after the second world war. The rapid spreading capability of the virus has compelled the world population to maintain strict preventive measures. The outrage of the virus has rampaged through the healthcare sector tremendously. This pandemic created a huge demand for necessary healthcare equipment, medicines along with the requirement for advanced robotics and artificial intelligence-based applications. The intelligent robot systems have great potential to render service in diagnosis, risk assessment, monitoring, telehealthcare, disinfection, and several other operations during this pandemic which has helped reduce the workload of the frontline workers remarkably. The long-awaited vaccine discovery of this deadly virus has also been greatly accelerated with AI-empowered tools. In addition to that, many robotics and Robotics Process Automation platforms have substantially facilitated the distribution of the vaccine in many arrangements pertaining to it. These forefront technologies have also aided in giving comfort to the people dealing with less addressed mental health complicacies. This paper investigates the use of robotics and artificial intelligence-based technologies and their applications in healthcare to fight against the COVID-19 pandemic. A systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method is conducted to accumulate such literature, and an extensive review on 147 selected records is performed.

Standard for the Quantification of a Sterilization Effect Using an Artificial Intelligence Disinfection Robot

Recent outbreaks and the worldwide spread of COVID-19 have challenged mankind with unprecedented difficulties. The introduction of autonomous disinfection robots appears to be indispensable as consistent sterilization is in desperate demand under limited manpower. In this study, we developed an autonomous navigation robot capable of recognizing objects and locations with a high probability of contamination and capable of providing quantified sterilization effects. In order to quantify the 99.9% sterilization effect of various bacterial strains, as representative contaminants with robots operated under different modules, the operating parameters of the moving speed, distance between the sample and the robot, and the radiation angle were determined. We anticipate that the sterilization effect data we obtained with our disinfection robot, to the best of our knowledge, for the first time, will serve as a type of stepping stone, leading to practical applications at various sites requiring disinfection.

Tele-Operated Oropharyngeal Swab (TOOS) Robot Enabled by TSS Soft Hand for Safe and Effective Sampling

The COVID-19 pandemic has imposed serious challenges in multiple perspectives of human life. To diagnose COVID-19, oropharyngeal swab (OP SWAB) sampling is generally applied for viral nucleic acid (VNA) specimen collection. However, manual sampling exposes medical staff to a high risk of infection. Robotic sampling is promising to mitigate this risk to the minimum level, but traditional robot suffers from safety, cost, and control complexity issues for wide-scale deployment. In this work, we present soft robotic technology is promising to achieve robotic OP swab sampling with excellent swab manipulability in a confined oral space and works as dexterous as existing manual approach. This is enabled by a novel Tstone soft (TSS) hand, consisting of a soft wrist and a soft gripper, designed from human sampling observation and bio-inspiration. TSS hand is in a compact size, exerts larger workspace, and achieves comparable dexterity compared to human hand. The soft wrist is capable of agile omnidirectional bending with adjustable stiffness. The terminal soft gripper is effective for disposable swab pinch and replacement. The OP sampling force is easy to be maintained in a safe and comfortable range (throat sampling comfortable region) under a hybrid motion and stiffness virtual fixture-based controller. A dedicated 3 DOFs RCM platform is used for TSS hand global positioning. Design, modeling, and control of the TSS hand are discussed in detail with dedicated experimental validations. A sampling test based on human tele-operation is processed on the oral cavity model with excellent success rate. The proposed TOOS robot demonstrates a highly promising solution for tele-operated, safe, cost-effective, and quick deployable COVID-19 OP swab sampling.

Design of Novel End-effectors for Robot-assisted Swab Sampling to Combat Respiratory Infectious Diseases

The COVID-19 outbreak has caused the mortality worldwide and the use of swab sampling is a common way of screening and diagnosis. To combat respiratory infectious diseases and assist sampling, robots have been utilized and shown promising potentials. Nonetheless, a safe, patient-friendly, and low-cost swabbing system would be crucial for the practical implementation of robots in hospitals or inspection stations. In this study, we proposed two recyclable and cost-efficient end-effector designs that can be equipped at the distal end of a robot to passively regulate or actively sense the force exerted onto patients. One way is to introduce passive compliant mechanisms with soft material to increase the flexibility of the swabbing system, while the other way is utilizing a force-sensing gripper with embedded optoelectronic sensors to actively sense the force or torque. The proposed designs were modelled computationally and tested experimentally. It is identified that the passive compliant mechanisms can increase the flexibility of the swabbing system when subjected to the lateral force and mitigate the vertical force resulted from buckling. The lateral force range that the force-sensing gripper can detect is 0-0.35 N and the vertical force range causing buckling effect that can be sensed by gripper is 1.5-2.5 N.

Public Perception and Reception of Robotic Applications in Public Health Emergencies Based on a Questionnaire Survey Conducted during COVID-19

Various intelligent technologies have been applied during COVID-19, which has become a worldwide public health emergency and brought significant challenges to the medical systems around the world. Notably, the application of robots has played a role in hospitals, quarantine facilities and public spaces and has attracted much attention from the media and the public. This study is based on a questionnaire survey on the perception and reception of robots used for medical care in the pandemic among the Chinese population. A total of 1667 people participated in the survey, 93.6% of respondents were pursuing or had completed a bachelor, master or even doctorate degree. The results show that Chinese people generally held positive attitudes towards "anti-pandemic robots" and affirmed their contribution to reducing the burden of medical care and virus transmission. A few respondents were concerned about the issues of robots replacing humans and it was apparent that their ethical views on robots were not completely consistent across their demographics (e.g., age, industry). Nevertheless, most respondents tended to be optimistic about robot applications and dialectical about the ethical issues involved. This is related to the prominent role robots played during the pandemic, the Chinese public's expectations of new technologies and technology-friendly public opinion in China. Exploring the perception and reception of anti-pandemic robots in different countries or cultures is important because it can shed some light on the future applications of robots, especially in the field of infectious disease control.

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human-robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.