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Automatic Tuberculosis and COVID-19 cough classification using deep learning
This cough data include 1. 68 hours of TB coughs, 18. 54 minutes of COVID-19 coughs and 1. 69 hours of healthy coughs from 47 TB patients, 229 COVID-19 patients and 1498 healthy patients and were used to train and evaluate a CNN, LSTM and Resnet50.
Accelerometer-based Bed Occupancy Detection for Automatic, Non-invasive Long-term Cough Monitoring
When integrated into a complete cough monitoring system, the daily cough rate of a patient undergoing TB treatment was determined over a period of 14 days.
Wake-Cough: cough spotting and cougher identification for personalised long-term cough monitoring
We present `wake-cough', an application of wake-word spotting to coughs using a Resnet50 and the identification of coughers using i-vectors, for the purpose of a long-term, personalised cough monitoring system.
Automatic non-invasive Cough Detection based on Accelerometer and Audio Signals
We present an automatic non-invasive way of detecting cough events based on both accelerometer and audio signals.
Deep Neural Network based Cough Detection using Bed-mounted Accelerometer Measurements
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.
