Table of Contents
Journal of Medical Engineering
Volume 2014 (2014), Article ID 951621, 9 pages
Research Article

Automated Cough Assessment on a Mobile Platform

1Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA
2School of Nursing, University of Rochester, Rochester, NY 14627, USA

Received 10 February 2014; Revised 8 July 2014; Accepted 9 July 2014; Published 10 August 2014

Academic Editor: Radovan Zdero

Copyright © 2014 Mark Sterling et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The development of an Automated System for Asthma Monitoring (ADAM) is described. This consists of a consumer electronics mobile platform running a custom application. The application acquires an audio signal from an external user-worn microphone connected to the device analog-to-digital converter (microphone input). This signal is processed to determine the presence or absence of cough sounds. Symptom tallies and raw audio waveforms are recorded and made easily accessible for later review by a healthcare provider. The symptom detection algorithm is based upon standard speech recognition and machine learning paradigms and consists of an audio feature extraction step followed by a Hidden Markov Model based Viterbi decoder that has been trained on a large database of audio examples from a variety of subjects. Multiple Hidden Markov Model topologies and orders are studied. Performance of the recognizer is presented in terms of the sensitivity and the rate of false alarm as determined in a cross-validation test.