# Objective Study of Sensor Relevance for Automatic Cough Detection

arXiv: https://arxiv.org/abs/2001.00537 · HTML: https://mlsanity.com/paper/2001.00537/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2019.

## Built on by (most cited first, 8 of 8)

- [Deep Neural Network based Cough Detection using Bed-mounted Accelerometer Measurements](https://mlsanity.com/paper/2102.04997/) (2021)
- [Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey](https://mlsanity.com/paper/2309.14383/) (2023)
- [Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models](https://mlsanity.com/paper/2311.06707/) (2023)
- [Automatic non-invasive Cough Detection based on Accelerometer and Audio Signals](https://mlsanity.com/paper/2109.00103/) (2021)
- [XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization](https://mlsanity.com/paper/2508.14949/) (2025)
- [How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough Detection Algorithm Performance](https://mlsanity.com/paper/2406.01529/) (2024)
- [A XAI-based Framework for Frequency Subband Characterization of Cough Spectrograms in Chronic Respiratory Disease](https://mlsanity.com/paper/2508.16237/) (2025)
- [From Sound to Symptom: Real-Time Respiratory Signal Understanding for Conversational Healthcare Agents](https://mlsanity.com/paper/2608.26163/) (2026)

## Abstract (arXiv)

The development of a system for the automatic, objective and reliable detection of cough events is a need underlined by the medical literature for years. The benefit of such a tool is clear as it would allow the assessment of pathology severity in chronic cough diseases. Even though some approaches have recently reported solutions achieving this task with a relative success, there is still no standardization about the method to adopt or the sensors to use. The goal of this paper is to study objectively the performance of several sensors for cough detection: ECG, thermistor, chest belt, accelerometer, contact and audio microphones. Experiments are carried out on a database of 32 healthy subjects producing, in a confined room and in three situations, voluntary cough at various volumes as well as other event categories which can possibly lead to some detection errors: background noise, forced expiration, throat clearing, speech and laugh. The relevance of each sensor is evaluated at three stages: mutual information conveyed by the features, ability to discriminate at the frame level cough from these latter other sources of ambiguity, and ability to detect cough events. In this latter experiment, with both an averaged sensitivity and specificity of about 94.5%, the proposed approach is shown to clearly outperform the commercial Karmelsonix system which achieved a specificity of 95.3% and a sensitivity of 64.9%.