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A Real-time Robot-based Auxiliary System for Risk Evaluation of COVID-19 Infection

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Document pages: 5 pages

Abstract: In this paper, we propose a real-time robot-based auxiliary system for riskevaluation of COVID-19 infection. It combines real-time speech recognition,temperature measurement, keyword detection, cough detection and other functionsin order to convert live audio into actionable structured data to achieve theCOVID-19 infection risk assessment function. In order to better evaluate theCOVID-19 infection, we propose an end-to-end method for cough detection andclassification for our proposed system. It is based on real conversation datafrom human-robot, which processes speech signals to detect cough and classifiesit if detected. The structure of our model are maintained concise to beimplemented for real-time applications. And we further embed this entireauxiliary diagnostic system in the robot and it is placed in the communities,hospitals and supermarkets to support COVID-19 testing. The system can befurther leveraged within a business rules engine, thus serving as a foundationfor real-time supervision and assistance applications. Our model utilizes apretrained, robust training environment that allows for efficient creation andcustomization of customer-specific health states.

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