Cover your cough: detection of respiratory events with confidence using a smartwatch

Khuong An Nguyen, Zhiyuan Luo
; Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications, PMLR 91:114-131, 2018.

Abstract

Cough and sneeze are the most common means to spread respiratory diseases amongst humans. Existing approaches to detect coughing and sneezing events are either intrusive or do not provide any reliability measure. This paper offers a novel proposal to reliably and non-intrusively detect such events using a smartwatch as the underlying hardware, Conformal Prediction as the underlying software. We rigorously analysed the performances of our proposal with the Harvard ESC Environmental Sound dataset, and real coughing samples taken from a smartwatch in different ambient noises.

Cite this Paper


BibTeX
@InProceedings{pmlr-v91-nguyen18a, title = {Cover your cough: detection of respiratory events with confidence using a smartwatch}, author = {Khuong An Nguyen and Zhiyuan Luo}, booktitle = {Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications}, pages = {114--131}, year = {2018}, editor = {Alex Gammerman and Vladimir Vovk and Zhiyuan Luo and Evgueni Smirnov and Ralf Peeters}, volume = {91}, series = {Proceedings of Machine Learning Research}, month = {11--13 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v91/nguyen18a/nguyen18a.pdf}, url = {http://proceedings.mlr.press/v91/nguyen18a.html}, abstract = {Cough and sneeze are the most common means to spread respiratory diseases amongst humans. Existing approaches to detect coughing and sneezing events are either intrusive or do not provide any reliability measure. This paper offers a novel proposal to reliably and non-intrusively detect such events using a smartwatch as the underlying hardware, Conformal Prediction as the underlying software. We rigorously analysed the performances of our proposal with the Harvard ESC Environmental Sound dataset, and real coughing samples taken from a smartwatch in different ambient noises.} }
Endnote
%0 Conference Paper %T Cover your cough: detection of respiratory events with confidence using a smartwatch %A Khuong An Nguyen %A Zhiyuan Luo %B Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications %C Proceedings of Machine Learning Research %D 2018 %E Alex Gammerman %E Vladimir Vovk %E Zhiyuan Luo %E Evgueni Smirnov %E Ralf Peeters %F pmlr-v91-nguyen18a %I PMLR %J Proceedings of Machine Learning Research %P 114--131 %U http://proceedings.mlr.press %V 91 %W PMLR %X Cough and sneeze are the most common means to spread respiratory diseases amongst humans. Existing approaches to detect coughing and sneezing events are either intrusive or do not provide any reliability measure. This paper offers a novel proposal to reliably and non-intrusively detect such events using a smartwatch as the underlying hardware, Conformal Prediction as the underlying software. We rigorously analysed the performances of our proposal with the Harvard ESC Environmental Sound dataset, and real coughing samples taken from a smartwatch in different ambient noises.
APA
Nguyen, K.A. & Luo, Z.. (2018). Cover your cough: detection of respiratory events with confidence using a smartwatch. Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications, in PMLR 91:114-131

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