# Automatic non-invasive Cough Detection based on Accelerometer and Audio Signals

arXiv: https://arxiv.org/abs/2109.00103 · HTML: https://mlsanity.com/paper/2109.00103/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 77 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings](https://mlsanity.com/paper/2012.01926/) (2020)
- [Automatic Cough Classification for Tuberculosis Screening in a Real-World Environment](https://mlsanity.com/paper/2103.13300/) (2021)
- [COVID-19 Detection in Cough, Breath and Speech using Deep Transfer Learning and Bottleneck Features](https://mlsanity.com/paper/2104.02477/) (2021)
- [DeepCough: A Deep Convolutional Neural Network in A Wearable Cough Detection System](https://mlsanity.com/paper/1509.02512/) (2015)
- [Objective Study of Sensor Relevance for Automatic Cough Detection](https://mlsanity.com/paper/2001.00537/) (2019)
- [Deep Neural Network based Cough Detection using Bed-mounted Accelerometer Measurements](https://mlsanity.com/paper/2102.04997/) (2021)

## Built on by (most cited first, 4 of 4)

- [Predicting Tuberculosis from Real-World Cough Audio Recordings and Metadata](https://mlsanity.com/paper/2307.04842/) (2023)
- [Wake-Cough: cough spotting and cougher identification for personalised long-term cough monitoring](https://mlsanity.com/paper/2110.03771/) (2021)
- [Automatic Tuberculosis and COVID-19 cough classification using deep learning](https://mlsanity.com/paper/2205.05480/) (2022)
- [Accelerometer-based Bed Occupancy Detection for Automatic, Non-invasive Long-term Cough Monitoring](https://mlsanity.com/paper/2202.03936/) (2022)

## Abstract (arXiv)

We present an automatic non-invasive way of detecting cough events based on both accelerometer and audio signals. The acceleration signals are captured by a smartphone firmly attached to the patient's bed, using its integrated accelerometer. The audio signals are captured simultaneously by the same smartphone using an external microphone. We have compiled a manually-annotated dataset containing such simultaneously-captured acceleration and audio signals for approximately 6000 cough and 68000 non-cough events from 14 adult male patients in a tuberculosis clinic. LR, SVM and MLP are evaluated as baseline classifiers and compared with deep architectures such as CNN, LSTM, and Resnet50 using a leave-one-out cross-validation scheme. We find that the studied classifiers can use either acceleration or audio signals to distinguish between coughing and other activities including sneezing, throat-clearing, and movement on the bed with high accuracy. However, in all cases, the deep neural networks outperform the shallow classifiers by a clear margin and the Resnet50 offers the best performance by achieving an AUC exceeding 0.98 and 0.99 for acceleration and audio signals respectively. While audio-based classification consistently offers a better performance than acceleration-based classification, we observe that the difference is very small for the best systems. Since the acceleration signal requires less processing power, and since the need to record audio is sidestepped and thus privacy is inherently secured, and since the recording device is attached to the bed and not worn, an accelerometer-based highly accurate non-invasive cough detector may represent a more convenient and readily accepted method in long-term cough monitoring.