Machine Learning Based Approaches for Cough Detection From Acceleration Signal

Abstract

The main goal of this research is to develop a a machine learning based method in order to detect cough from acceleration signals. In this study, two different methods are proposed: a conventional one that uses XGBoost as a classifier and a deep learning which uses CNN-1D as an architecture. We found that these models were able to distinguish between acceleration signals caused by coughing and acceleration signals caused by other activities such as clearing throat, talking, laughing and movements in different directions with high accuracy. This study affirms that cough monitoring based on accelerometer measurements generated by the Hexoskin device is possible, making it a new user-independent tool for cough detection.