RAPID: In-Home Automated and Non-Invasive Evaluation of COVID-19 Infection with Commodity Smartphones

抗击2019冠状病毒病（COVID-19）大流行的关键是防止公共卫生系统超载，以便为住院患者提供足够的医疗资源。该项目将开发新的移动传感和人工智能（AI）技术，用于家庭评估COVID-19感染，以便对潜在的病毒性疾病携带者进行自动化和无创筛查。旨在及时发现由症状相似的其他疾病引起的阴性病例，从而尽可能避免不必要的医院就诊。拟议的技术将使用商品智能手机来测量与COVID-19感染独特相关的人类气道力学的变化。这些测量基于智能手机内置扬声器和麦克风的声学传感。首先，将设计新的声波波形，以尽量减少人体气道中的声信号失真。其次，新的信号处理技术将被开发用于精确测量。第三，深度学习技术将用于开发描述气道力学核心特征的通用模型。这些技术将通过实验室测试和学生志愿者实验进行评估。这项研究将有助于在诊所外识别COVID-19感染的假阳性，并有助于遏制病毒的传播和破坏。所提出的技术将适用于各种各样的商品智能手机，也可以在日常实践中使用手工制作的话筒。各种教育和外联活动也将产生更广泛的影响。将编制新的课程以纳入这项研究的成果，并将通过向工业转让技术来传播研究成果。该项目的成果，包括源代码和从学生志愿者那里收集的数据，将在项目存储库（http://www.pitt.edu/~weigao/research_COVID19.html）中保存至少五年，并将向公共社区提供。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A key to combat the Coronavirus Disease (COVID-19) pandemic is to prevent the pandemic from overloading the public healthcare system, so that sufficient medical resources could be available for hospitalized patients. This project will develop new mobile sensing and Artificial Intelligence (AI) techniques for in-home evaluation of COVID-19 infection in order to pursue automated and non-invasive screening of potential viral disease carriers. It aims to timely identify negative cases caused by other diseases with similar symptoms, and hence avoids unnecessary hospital visits as many as possible. The proposed techniques will use commodity smartphones to measure the changes of humans’ airway mechanics, which are uniquely correlated to COVID-19 infection. These measurements build on acoustic sensing with smartphones’ built-in speakers and microphones. First, new acoustic waveforms will be designed to minimize acoustic signal distortion in human airways. Second, new signal processing techniques will be developed for accurate measurements. Third, deep learning techniques will be used to develop generic models that depict the core characteristics of airway mechanics. These techniques will be evaluated by lab testing and experiments with student volunteers. This research will enable identifying false positives of COVID-19 infection out of the clinic and could contribute to the containment of the virus spread and damage. The proposed technologies will be applicable to a wide variety of commodity smartphones and could also be used in daily practice with handmade mouthpieces. Broader impacts will also result from a variety of education and outreach activities. New courses will be developed to incorporate the outcome of this research, and the research outcome will be disseminated through technology transfer to industry. The outcome of this project, including source codes and collected data from student volunteers, will be maintained at the project repository (http://www.pitt.edu/~weigao/research_COVID19.html) for at least five years, and will be made available to the public community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.