论文标题
基于机器人的实时辅助系统，用于评估Covid-19感染
A Real-time Robot-based Auxiliary System for Risk Evaluation of COVID-19 Infection
论文作者
论文摘要
在本文中，我们提出了一个基于实时机器人的辅助系统，以评估Covid-19感染的风险。它结合了实时语音识别，温度测量，关键字检测，咳嗽检测和其他功能，以将实时音频转换为可行的结构化数据，以实现COVID-19感染风险评估功能。为了更好地评估COVID-19的感染，我们提出了一种针对我们提出的系统的止咳饮用和分类的端到端方法。它基于来自人类机器人的真实对话数据，该数据处理语音信号以检测咳嗽并在检测到的情况下进行分类。我们的模型的结构保持简洁，以实现实时应用程序。我们将整个辅助诊断系统进一步嵌入到机器人中，并将其放置在社区，医院和超市，以支持Covid-19测试。该系统可以在业务规则引擎中进一步利用，从而成为实时监督和援助应用程序的基础。我们的模型利用了一个预处理的，可靠的培训环境，可以有效地创建和自定义客户特定的健康状况。
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.
