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SymListener: Detecting Respiratory Symptoms via Acoustic Sensing in Driving Environments

Published: 14 January 2023 Publication History

Abstract

Sound-related respiratory symptoms are commonly observed in our daily lives. They are closely related to illnesses, infections, or allergies but ignored by the majority. Existing detection methods either depend on specific devices, which are inconvenient to wear, or are sensitive to noises and only work for indoor environment. Considering the lack of monitoring method for in-car environment, where there is high risk of spreading infectious diseases, we propose a smartphone-based system, named SymListener, to detect respiratory symptoms in driving environment. By continuously recording acoustic data through a built-in microphone, SymListener can detect the sounds of cough, sneeze, and sniffle. We design a modified ABSE-based method to remove the strong and changeable driving noises while saving energy of the smartphone. An LSTM network is adopted to classify the three types of symptoms according to the carefully designed acoustic features. We implement SymListener on different Android devices and evaluate its performance in real driving environment. The evaluation results show that SymListener can reliably detect target respiratory symptoms with an average accuracy of 92.19% and an average precision of 90.91%.

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Published In

cover image ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks  Volume 19, Issue 1
February 2023
565 pages
ISSN:1550-4859
EISSN:1550-4867
DOI:10.1145/3561987
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

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Publication History

Published: 14 January 2023
Online AM: 15 July 2022
Accepted: 02 February 2022
Revised: 11 December 2021
Received: 01 September 2021
Published in TOSN Volume 19, Issue 1

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Author Tags

  1. Respiratory symptom detection
  2. acoustic sensing
  3. smartphone application

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  • Refereed

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  • National Natural Science Foundation of China (NSFC)

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  • (2024)Unsupervised Adversarial Example Detection of Vision Transformers for Trustworthy Edge ComputingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3674981Online publication date: 2-Jul-2024
  • (2024)Suitable and Style-Consistent Multi-Texture Recommendation for Cartoon IllustrationsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365251820:7(1-26)Online publication date: 16-May-2024
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