Deep Neural Network based Cough Detection using Bed-mounted Accelerometer Measurements

TL;DR

This study demonstrates that bed-mounted accelerometers combined with deep neural networks can accurately detect coughs, offering a privacy-preserving, non-intrusive alternative to audio-based cough monitoring.

Contribution

It introduces a novel approach using bed-mounted accelerometers and deep learning models for high-accuracy cough detection, addressing privacy and convenience concerns.

Findings

Resnet50 achieved AUC > 0.98 in cough detection

All tested classifiers distinguished coughs from other activities with high accuracy

The method enables privacy-preserving, non-intrusive long-term cough monitoring

Abstract

We have performed cough detection based on measurements from an accelerometer attached to the patient's bed. This form of monitoring is less intrusive than body-attached accelerometer sensors, and sidesteps privacy concerns encountered when using audio for cough detection. For our experiments, we have compiled a manually-annotated dataset containing the acceleration signals of approximately 6000 cough and 68000 non-cough events from 14 adult male patients in a tuberculosis clinic. As classifiers, we have considered convolutional neural networks (CNN), long-short-term-memory (LSTM) networks, and a residual neural network (Resnet50). We find that all classifiers are able to distinguish between the acceleration signals due to coughing and those due to other activities including sneezing, throat-clearing and movement in the bed with high accuracy. The Resnet50 performs the best, achieving an area under the ROC curve (AUC) exceeding 0.98 in cross-validation experiments. We conclude that high-accuracy cough monitoring based only on measurements from the accelerometer in a consumer smartphone is possible. Since the need to gather audio is avoided and therefore privacy is inherently protected, and since the accelerometer is attached to the bed and not worn, this form of monitoring may represent a more convenient and readily accepted method of long-term patient cough monitoring.