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

• URL: http://arxiv.org/abs/2008.07695v1
• Date: Tue, 18 Aug 2020 01:58:52 GMT
• Title: A Real-time Robot-based Auxiliary System for Risk Evaluation of COVID-19 Infection
• Authors: Wenqi Wei, Jianzong Wang, Jiteng Ma, Ning Cheng, Jing Xiao
• Abstract summary: 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.
• Score: 31.76845310194196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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