Automated Cough Assessment on a Mobile Platform
Open accessAbstract
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.
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Studies demonstrate that the wearable chest device ADAMM can be used to track adolescent asthma.
“Studies demonstrate that the wearable chest device, ADAMM, can be used to track adolescent asthma [26-28].”
From Wearables and the Medical Revolution (2018) · cites this paper as [28]
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Research works printed but not linked: 1
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Wearables and the Medical Revolution
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Self-citation
- Authors
- Sterling, M., Rhee, H., & Bocko, M.
- Affiliations
- University of Rochester (US)
- University of Rochester Medicine (US)
- Corresponding author
- Rhee, Hyekyun
- Venue
- Journal of Medical Engineering
- Publisher
- Wiley
- Published
- 2014-08-10
- Volume
- 2014
- Pages
- 1-9
- ISSN
- 2314-5129 (ISSN-L)
- 2314-5137
- PMC
- PMC4264627
- OpenAlex
- W2103364582
- Cited by
- 31
- References
- 29
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 3 · 2022: 2 · 2021: 4 · 2020: 6 · 2019: 3 · 2018: 2 · 2017: 2 · 2016: 3 · 2015: 3
- FWCI
- 2.14
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Medical Engineering (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5RO1NR11169)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Respiratory and Cough-Related Research
- Keywords
- asthma monitoring, mobile health, audio feature extraction, hidden Markov models, Viterbi decoding, speech recognition, symptom monitoring, false alarm rate
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