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Related Experiment Video

Updated: Apr 19, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Automated Cough Assessment on a Mobile Platform.

Mark Sterling1, Hyekyun Rhee2, Mark Bocko1

  • 1Department of Electrical and Computer Engineering, University of Rochester.

Journal of Medical Engineering
|December 16, 2014
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Summary

An Automated System for Asthma Monitoring (ADAM) uses a mobile app and microphone to detect cough sounds. This system aids healthcare providers by recording symptom tallies and audio data for review.

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Area of Science:

  • Biomedical Engineering
  • Computational Linguistics
  • Medical Informatics

Background:

  • Asthma monitoring traditionally relies on patient-reported symptoms, which can be subjective.
  • Objective, continuous monitoring of asthma exacerbations is crucial for timely intervention.
  • Existing remote monitoring solutions often lack automated, objective symptom detection.

Purpose of the Study:

  • To develop and evaluate an Automated System for Asthma Monitoring (ADAM).
  • To assess the feasibility of using mobile device audio analysis for cough detection in asthma.
  • To provide healthcare providers with objective data for asthma management.

Main Methods:

  • Development of a custom mobile application integrated with a user-worn microphone.
  • Audio signal acquisition and processing for cough sound identification.
  • Utilizing speech recognition and machine learning, specifically Hidden Markov Models (HMMs), for cough detection.
  • Training HMMs on a diverse database of audio examples and evaluating performance using sensitivity and false alarm rates.

Main Results:

  • The Automated System for Asthma Monitoring (ADAM) successfully acquires and processes audio signals.
  • A cough detection algorithm based on Hidden Markov Models was developed and trained.
  • Performance metrics including sensitivity and false alarm rates were determined through cross-validation.
  • Symptom tallies and raw audio waveforms are recorded for healthcare provider review.

Conclusions:

  • The Automated System for Asthma Monitoring (ADAM) presents a novel approach for objective asthma symptom tracking.
  • Mobile-based audio analysis shows promise for detecting coughs associated with asthma exacerbations.
  • This system has the potential to enhance remote patient monitoring and clinical decision-making in asthma care.