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

RAPID：使用商用智能手机对 COVID-19 感染进行家庭自动化和非侵入性评估

• 批准号: 2029520
• 负责人: Wei Gao
• 金额: $ 20万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029520&HistoricalAwards=false
• 关键词: RAPID; Home; Automated; Non; Invasive

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.

抗击冠状病毒病(新冠肺炎)大流行的一个关键是防止大流行使公共医疗系统不堪重负，以便能够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，用于对新冠肺炎感染的家庭评估，以实现对潜在病毒携带者的自动化和非侵入性筛查。它旨在及时识别由其他具有类似症状的疾病导致的阴性病例，从而尽可能避免不必要的医院就诊。拟议的技术将使用商用智能手机来测量人类呼吸道力学的变化，这些变化与新冠肺炎感染独特相关。这些测量建立在智能手机内置扬声器和麦克风的声学传感基础上。首先，将设计新的声学波形，将人体呼吸道的声学信号失真降至最低。第二，将开发新的信号处理技术，以实现准确的测量。第三，深度学习技术将用于开发描述呼吸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的实验进行评估。这项研究将能够在临床之外识别新冠肺炎感染的假阳性，并可能有助于遏制病毒的传播和损害。拟议的技术将适用于各种商品智能手机，也可以用于手工制作的话筒的日常实践中。各种教育和外联活动也将产生更广泛的影响。将开发新的课程以纳入这项研究的成果，并将通过向工业界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，将在项目库(http://www.pitt.edu/~weigao/research_COVID19.html)保存至少五年，并将向公众开放。该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

AiFi: AI-Enabled WiFi Interference Cancellation with Commodity PHY-Layer Information

• DOI: 10.1145/3560905.3568537
• 发表时间: 2022-11
• 期刊: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
• 作者: Ruirong Chen;Kai Huang;Wei Gao

RAScatter: Achieving Energy-Efficient Backscatter Readers via AI-Assisted Power Adaptation

• DOI: 10.1109/iotdi54339.2022.00016
• 发表时间: 2022-05
• 期刊: 2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI)
• 作者: Kai Huang;Ruirong Chen;Wei Gao

SpiroSonic: monitoring human lung function via acoustic sensing on commodity smartphones

• DOI: 10.1145/3372224.3419209
• 发表时间: 2020-09
• 期刊: Proceedings of the 26th Annual International Conference on Mobile Computing and Networking
• 作者: Xingzhe Song;Boyuan Yang;Ge Yang;Ruirong Chen;E. Forno;Wei Chen;Wei Gao

Out-Clinic Pulmonary Disease Evaluation via Acoustic Sensing and Multi-Task Learning on Commodity Smartphones

通过商品智能手机上的声学传感和多任务学习进行临床外肺部疾病评估

• DOI: 10.1145/3560905.3568437
• 发表时间: 2022
• 期刊: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
• 作者: Yin, Xiangyu;Huang, Kai;Forno, Erick;Chen, Wei;Huang, Heng;Gao, Wei
• 通讯作者: Gao, Wei

Real-time neural network inference on extremely weak devices: agile offloading with explainable AI

• DOI: 10.1145/3495243.3560551
• 发表时间: 2022-10
• 期刊: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking
• 作者: Kai Huang;Wei Gao