Project Description:

The key to combat the 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 being infected. To avoid such overload, false positives of COVID-19 infection should be detected out of clinic, to avoid unnecessary hospital visits as many as possible. Such detection, however, is difficult because COVID-19 shares many symptoms in common with other diseases, such as ear infection that causes fever and respiration diseases that cause shortness of breath.

The goal of this research is to address the aforementioned difficulty by developing new mobile sensing and AI techniques that use commodity smartphones to measure the changes of humans' airway mechanics, which are uniquely correlated to COVID-19 infection. More specifically, since COVID-19 infection causes pathological changes of lung alveoli and bronchi, it could result in narrowed bronchi, airway inflammation and mucus that can be used as more reliable indicators to evaluate such infection. We propose to measure these indicators by transmitting acoustic signals with the smartphones' built-in speakers and analyzing the received acoustic signal after it is being reflected by the airway. We aim to ensure accuracy, reliability and generality of these measurements over actual human bodies. First, we will design new acoustic waveforms to minimize the distortion of acoustic signal when being propagated throughout the human airway system. Second, we will develop new signal processing techniques to appropriately analyze the received signal and evaluate the change of mechanics throughout the entire human airway system. Third, to ensure that our proposed technique can be widely applied to big crowds of people, we will use deep learning techniques to develop generic models that depict the core characteristics of humans' airway mechanics. The proposed technologies will be implemented and evaluated by both lab-controlled testing and medium-scale experimentation with student volunteers.

 

Selected publications (Complete List):

  1. MyoMonitor: Evaluating Muscle Fatigue with Commodity Smartphones [pdf]
    Xingzhe Song, Hongshuai Li and Wei Gao, Elsevier Smart Health, vol. 19, article No. 100175, 2021.

  2. SpiroSonic: Monitoring Human Lung Function via Acoustic Sensing on Commodity Smartphones [pdf]
    Xingzhe Song, Boyuan Yang, Ge Yang, Ruirong Chen, Erick Forno, Wei Chen and Wei Gao, in Proceedings of the 26th International Conference on Mobile Computing and Networking (MobiCom), 2020.
    (Acceptance Ratio: 62/384=16.1%)

 

Participants:

 

Educational Activities:

The outcome of this research has been integrated into the curriculum of multiple undegraduate courses at Pitt, including ECE1160: Embedded Computer System Design I and ECE 1175: Embedded Systems Design, to provide undergraduate students with pioneering contents and topics for their semester-long course projects.

 

Broader Impact Outcomes:

The PIs are currently working with the Children's Hospital of Pittsburgh to develop mobile systems that allow in-home self-evaluation of possible COVID-19 infections through commodity smartphones. This research has attracted wide attention from the public media [WGN TV, Daily Mail, News Medical, Medical Express, Pittsburgh Post-Gazette].


This material is based upon work supported by the National Science Foundation under Grant No. 2029520. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).