A Real-time Robot-based Auxiliary System for Risk Evaluation of COVID-19 Infection
This addresses the need for automated, real-time COVID-19 screening in public settings, though it appears incremental as it combines existing technologies into a robot-based system.
The paper tackles the problem of COVID-19 infection risk assessment by proposing a real-time robot-based auxiliary system that integrates speech recognition, temperature measurement, and cough detection to convert live audio into structured data, achieving the risk evaluation function and being deployed in communities, hospitals, and supermarkets.
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-specific health states.