Mobicough: Real-time cough detection and monitoring using low-cost mobile devices

Abstract

In this paper we present MobiCough, a method and system for cough detection and monitoring on low-cost mobile devices in real-time. MobiCough utilizes the acoustic data stream captured from a wirelessly low-cost microphone worn on user’s collar and connected to the mobile device via Bluetooth. MobiCough detects the cough in four steps: sound pre-processing, segmentation, feature & event extraction, and cough prediction. In addition, we propose the use of a simple yet effective robust to noise predictive model that combines Gaussian Mixture model and Universal Background model (GMM-UBM) for predicting cough sounds. The proposed method is rigorously evaluated through a dataset consisting of more than 1000 cough events and a significant number of noises. The results demonstrate that cough can be detected with the precision and recall of more than 91 % with individually trained models and over 81 % for subject independent training. These results are really potential for health-care applications acquiring cough detection and monitoring using low-cost mobile devices.