mLung: Privacy-Preserving Naturally Windowed Lung Activity Detection for Pulmonary Patients

摘要

mLung is a privacy preserving, naturally windowed, mobile-cloud hybrid pulmonary care service for detecting unusual lung sounds like coughing and wheezing from streaming audio and inertial sensor data from a smartphone for pulmonary patients. mLung employs a combination of: (1) natural windowing of audio data from the patient respiration cycle captured by the inertial sensors, (2) in-phone speech detection and filtering by a lightweight classifier for patient privacy, and (3) in-cloud lung and confounding sound classification by a heavyweight and expert supervised classifier. This paper describes the design and architecture of mLung and using novel lung activity data collected by smartphone from 131 patients and healthy subjects, provides empirical evidence that mLung is 15%-25% more accurate in detecting lung sounds when compared to a state-of-the-art phone based internal body sound detection system using specialized microphone hardware, with a best f-1 score of 98%.