ABSTRACT

Coronavirus disease (COVID-19) pandemic has intensively damaged human socio-economic lives and the growth of countries around the world. Many efforts have been made in the direction of artificial intelligence (AI) techniques to detect the corona at an early stage and take necessary precautions to stop it from spreading or recovery from the infection. However, the situation and solutions are still challenging. In this paper, we proposed various technological aspects, solutions using a supervised/unsupervised manner and continuous health monitoring with physiological parameters. Finally, the performance of COVID-19 detection with Gaussian mixture model-universal background model (GMM-UBM) technique using the voice signal has been demonstrated. The developed system achieves the COVID-19 detection performance in terms of areas under receiver operating characteristic (ROC) curves in the range 60–67%. Moreover, the various lessons learned from the current COVID-19 crisis are presented for future directions.

摘要

冠状病毒病 (COVID-19) 大流行严重破坏了人类的社会经济生活和世界各国的经济增长。在人工智能 (AI) 技术的方向上已经做出了许多努力，以便在早期阶段检测到电晕并采取必要的预防措施来阻止它传播或从感染中恢复。然而，情况和解决方案仍然具有挑战性。在本文中，我们提出了各种技术方面、使用监督/非监督方式的解决方案以及使用生理参数进行持续健康监测。最后，演示了使用语音信号的高斯混合模型-通用背景模型 (GMM-UBM) 技术检测 COVID-19 的性能。开发的系统在接受者操作特征（ROC）曲线下的面积方面达到了 60-67% 范围内的 COVID-19 检测性能。此外，还介绍了从当前 COVID-19 危机中吸取的各种教训，以供未来发展。