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

Khuong An Nguyen; Zhiyuan Luo

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

Research output: Contribution to conference › Paper › peer-review

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.

Original language	English
Publication status	Published - 1 Jun 2018

Access to Document

https://cml.rhul.ac.uk/copa2018/

Cite this

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title = "Cover your cough: Detection of respiratory events with confidence using a smartwatch",

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.",

author = "Nguyen, {Khuong An} and Zhiyuan Luo",

note = "7th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2018). ; Conference date: 11-06-2018 Through 13-06-2018",

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AU - Luo, Zhiyuan

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PY - 2018/6/1

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N2 - 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.

AB - 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.

M3 - Paper

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