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.
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Pham, C. (2016). MobiCough: Real-Time Cough Detection and Monitoring Using Low-Cost Mobile Devices. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_29
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DOI: https://doi.org/10.1007/978-3-662-49381-6_29
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