Development of an all-in-one soft wearable device for accurate lung function detection and asthma diagnosis

PROJECT SUMMARY The extensive heterogeneity with differing underlying pathological processes and symptoms that overlap with a wide range of disease processes make correct diagnosis of asthma difficult and contribute to the challenges in treatment and care of asthma. Consequently, confirming one’s diagnosis of asthma

involves a long and sophisticated diagnostic workflow involving multiple tests and interpretation of results, the process of which is available only to dedicated pulmonary care facilities. Instead, for majority of cases, patients with symptoms suspected of asthma visit primary care offices for initial assessment based on

office-based portable spirometers. However, recent studies show that many office-based spirometers showed variable performance in “real-world” settings and produced mixed results in primary care settings. Given the central role of spirometry data in the diagnosis of asthma and treatment decision, suboptimal

performance of spirometry in primary offices might contribute to misdiagnosis or delayed diagnosis. The main drawback of current portable spirometers is it requires extensive user focus and effort, such as expiring as quickly and long as possible, biting the mouthpiece and sealing it with the lips, and blocking

the nostrils with a nose clip, all contributing to test results with high inter-trial variability. The research team hypothesizes that a new measurement tool that is much less dependent on user’s effort and easier to use will result in the more consistent and reliable measurement of lung functions. Therefore, in this

study, we propose the following specific aims to develop a novel wearable sensor that can be attached to the user’s body to generate metrics comparable to standard lung function parameters (FVC, FEV1, and PEF): 1) Develop a soft wearable device for high-fidelity auscultation and chest movement detection. 2)

Correlate sound data and lung function parameters to develop a lung function estimation algorithm. 3) Validate device performance and usability in a clinical setting with patients. We will identify the correlation between the sensor data (lung/airway sounds and chest accelerometry) and standard lung function

parameters provided by a portable spirometer. Through this, a lung function estimation algorithm will be developed. A mobile application that processes the sensor data in real time will also be developed to enhance user engagement and to ensure each maneuver is performed correctly even in the absence of

human coaching. Feasibility to use the sensor system in a clinical environment as well as their performance as a reliable lung function assessment tool will be validated with patients visiting a doctor’s office for pulmonary issues. The successful completion of this study will lead to the development of a new

lung function assessment method that requires less user effort and is more reliable than spirometry performed in primary care settings. Advances in the mean to assess lung functions in primary care offices will improve the accuracy of asthma diagnosis, care, and ultimately patient outcome.