Cited By
View all- Wang YZhang XChakalasiya JXu XJiang YLi YPatel SShi Y(2022)HearCough: Enabling continuous cough event detection on edge computing hearablesMethods10.1016/j.ymeth.2022.05.002205(53-62)Online publication date: Sep-2022
Detecting Episodes of Increased Cough Using Kinetic Earables
Authors: 
Tobias Röddiger, 
Michael Beigl, Michael Hefenbrock, Daniel Wolffram, Erik PescaraAuthors Info & Claims
Tobias Röddiger, Michael Beigl, Michael Hefenbrock, Daniel Wolffram, Erik PescaraAuthors Info & ClaimsPages 111 - 115
Abstract
This paper introduces the detection of episodes of increased cough (e.g. during illness) based on cough event classification using kinetic earables. In a twelve subject study, we collected voluntary weak and strong cough as well as five non-cough activities (e.g., talking) under various conditions (e.g., walking). During the activities, an in-ear worn sensor records acceleration and gyroscope data. In total, we collected 4,200 activity samples. A single step classification pipeline (0.77 overall accuracy) serves as the foundation for statistical analysis to achieve episodes of increased cough discrimination. As a digression, we reverse data and perform pose classification which could enable faster cough episode prediction. All-in-all, earables might help to objectify illness to encourage formal diagnosis.
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- Detecting Episodes of Increased Cough Using Kinetic Earables
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February 2021
321 pages
ISBN:9781450384285
DOI:10.1145/3458709
Copyright © 2021 ACM.
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Published: 11 July 2021
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View all- Wang YZhang XChakalasiya JXu XJiang YLi YPatel SShi Y(2022)HearCough: Enabling continuous cough event detection on edge computing hearablesMethods10.1016/j.ymeth.2022.05.002205(53-62)Online publication date: Sep-2022
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