In this paper, we propose a real-time robot-based auxiliary system for risk evaluation of COVID-19 infection. It combines real-time speech recognition, temperature measurement, keyword detection, cough detection and other functions in order to convert live audio into actionable structured data to achieve the COVID-19 infection risk assessment function. In order to better evaluate the COVID-19 infection, we propose an end-to-end method for cough detection and classification for our proposed system. It is based on real conversation data from human-robot, which processes speech signals to detect cough and classifies it if detected. The structure of our model are maintained concise to be implemented for real-time applications. And we further embed this entire auxiliary diagnostic system in the robot and it is placed in the communities, hospitals and supermarkets to support COVID-19 testing. The system can be further leveraged within a business rules engine, thus serving as a foundation for real-time supervision and assistance applications. Our model utilizes a pretrained, robust training environment that allows for efficient creation and customization of customer-specic health states.

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Purpose This study aims to use mixed methods to create a new conceptual framework to understand the unique characteristics of virtual tourism experiences (VTE), which has not been systemically examined. Design/methodology/approach Study 1 uses topic modeling with Latent Dirichlet Allocation to analyze 91,609 online reviews from the Airbnb Experience platform. Study 2 uses content analysis of open-ended qualitative responses from VTE customers. The two studies together are used to build a new conceptual model. Findings Building upon the Stimulus–Organism–Response (S-O-R) model and the experience economy, results present a new conceptual model and identify VTE as unique in terms of Stimulus (education, entertainment, esthetics, escapism and connection), Organism (experiencing synchronicity, telepresence, participation and customization, emotion) and Response (evaluation and behavioral responses). Given the uniqueness of VTE, the new construct of the virtual servicescape is incorporated, recognizing the host, the focal customer and other customers, and the technology as the four main components. Practical implications The proposed framework can be used to guide the design, development, and evaluation of VTE, including identifying the key considerations, engagement within the ecosystem and providing guidance to hosts and operators. Originality/value To the best of the authors’ knowledge, this is the first study that systematically explores VTE and proposes the theoretical framework to comprehensively understand this new form of experience in sharing economy by combining the unique aspects of the stimulus, organism, response and virtual servicescape.

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The global outbreak of the COVID-19 in the worldwide has drawn lots of attention recently. The elderly are more vulnerable to COVID-19 and tend to have severe conditions and higher mortality as their immune function decreased and they are prone to having multiple chronic diseases. Therefore, avoiding viral infection, early detection and treatment of viral infection in the elderly are important measures to protect the safety of the elderly. In this paper, we propose a real-time robot-based COVID-19 detection system: Epidemic Guard. It combines speech recognition, keyword detection, cough classification, and medical services to convert real-time audio into structured data to record the user's real condition. These data can be further utilized by the rules engine to provide a basis for real-time supervision and medical services. In addition, Epidemic Guard comes with a powerful pre-training model to effectively customize the user's health status.