Machine Learning based Approaches for Cough Detection From Acceleration [r-libre/3133]

Belhaj Messaoud, Ines; Ben Cheikh, Elyes; Chiboub, Assaad; Loulou, Karim; Ouakrim, Youssef; Ben Jebara, Sofia; Dixon, Philippe C. et Mezghani, Neila (2023). Machine Learning based Approaches for Cough Detection From Acceleration. Dans IEEE 22th International Conference on Cyberworlds (CW2023). Sousse, Tunisia. https://doi.org/10.1109/CW58918.2023.00056

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Catégorie de document :	Communications dans des actes de congrès/colloques
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	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.
Adresse de la version officielle :	https://ieeexplore.ieee.org/abstract/document/1033...
Déposant:	Ayena, Johannes
Responsable :	Neila Mezghani
Dépôt :	29 janv. 2024 14:00
Dernière modification :	07 août 2024 13:25

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