mLung++: automated characterization of abnormal lung sounds in pulmonary patients using multimodal mobile sensors

The design of computational methods for detection of abnormal lung sounds (e.g., wheeze) associated with the advent of a pulmonary attack (e.g., asthma) and subsequent characterization of the 'severity' or progressive exacerbation in pulmonary patients is a relevant problem in ubiquitous computing. While a few recent works have been done on on-body sensor and smartphone sensor based lung activity detection, designing a comprehensive architecture for the detection and characterization of abnormal lung sounds (e.g., wheeze) is an open issue. In this paper, we present mLung++, which is a comprehensive pulmonary care system for respiration cycle based detection and subsequent characterization of wheezing in chronic pulmonary patients using audio and inertial sensors embedded on a smartphone. For the design, training, and evaluation, we use audio and Inertial Measurement Unit (IMU) data collected by smartphone and watch from 131 human subjects (91 pulmonary patients, 40 healthy control). We show empirical evidence that the performance of mLung++ for characterizing abnormal lung sounds (accuracy 93.4% and f1_score 77.94%) is comparable with that of high-quality on-body sensor based characterization, which is usually done in a hospital or clinical setup.