CoughBuddy: Multi-Modal Cough Event Detection Using Earbuds Platform

The prevalence of novel wearable devices has opened new horizons of opportunity for lung health monitoring and assessment in the past decade. There has been extensive amount of study on cough detection using acoustic features of the cough from smartphones and smartwatches. However, the specificity of the algorithms has always been a concern when exposed to the unseen field data that contain cough-like sounds. In this paper, we propose a novel sensor fusion algorithm that employs a hybrid of classification and template matching algorithms to tackle the problem of unseen classes. The algorithm utilizes in-ear audio signal as well as head motion captured by the inertial measurement unit (IMU). A large study including 45 subjects from healthy and chronic cough cohorts was conducted that contained various tasks including cough and cough-like body sounds in various conditions such as quite/noisy and stationary/non-stationary. Our proposed hybrid algorithm which comprises audio-event classification and a dynamic time warping (DTW)-based IMU template matching is evaluated for sensitivity and specificity in the aforementioned conditions using leave one-subject out validation (LOSOV). Our model is able to achieve an average sensitivity of 83% for stationary tasks with an average specificity of 91.7% for cough-like sounds reducing the false positive rate by 55%. These results indicate the feasibility and superiority of earbuds platforms for detection of pulmonary sound events such as cough.