ArticleJournal of medical engineering2014
Automated Cough Assessment on a Mobile Platform.
Article in Journal of medical engineering, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01696357 (Developing an Automated Symptom Monitoring Device for Adolescents With Asthma), which is not on this map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Developing an Automated Symptom Monitoring Device for Adolescents With Asthma
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Asthma Symptom Self-Monitoring Methods for Children and Adolescents: Present and Future.Children (Basel, Switzerland) · 2025Review
- Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.Journal of medical Internet research · 2024Article
- eHealth Technologies for Monitoring Pediatric Asthma at Home: Scoping Review.Journal of medical Internet research · 2023Article
- Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review.Sensors (Basel, Switzerland) · 2022Review
- Cough Sounds Recorded via Smart Devices as Useful Non-Invasive Digital Biomarkers of Aspiration Risk: A Case Report.Sensors (Basel, Switzerland) · 2021Article
- Noncontact Sensing of Contagion.Journal of imaging · 2021Review
- The present and future of cough counting tools.Journal of thoracic disease · 2020Review
- Side-Channel Sensing: Exploiting Side-Channels to Extract Information for Medical Diagnostics and Monitoring.IEEE journal of translational engineering in health and medicine · 2020Article
- Wearables and the medical revolution.Personalized medicine · 2018Review
- Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems.Healthcare informatics research · 2017Article
- Cough frequency monitors: can they discriminate patient from environmental coughs?Journal of thoracic disease · 2016Article
- Article
- Evaluating the Validity of an Automated Device for Asthma Monitoring for Adolescents: Correlational Design.Journal of medical Internet research · 2015Article
- Mobile Applications for Patient-centered Care Coordination: A Review of Human Factors Methods Applied to their Design, Development, and Evaluation.Yearbook of medical informatics · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.