BreathEasy: Assessing Respiratory Diseases Using Mobile Multimodal Sensors

Abstract

Mobil respiratory assessments using commodity smartphones and smartwatches are unmet needs for patient monitoring at home. In this paper, we show the feasibility of using multimodal sensors embedded in consumer mobile devices for non-invasive, low-effort respiratory assessment. We have conducted studies with 228 chronic respiratory patients and healthy subjects, and show that our model can estimate respiratory rate with mean absolute error (MAE) 0.72$\pm$0.62 breath per minute and differentiate respiratory patients from healthy subjects with 90% recall and 76% precision when the user breathes normally by holding the device on the chest or the abdomen for a minute. Holding the device on the chest or abdomen needs significantly lower effort compared to traditional spirometry which requires a specialized device and forceful vigorous breathing. This paper shows the feasibility of developing a low-effort respiratory assessment towards making it available anywhere, anytime through users' own mobile devices.