SymDetector: Detecting sound-related respiratory symptoms using smartphones

Xiao Sun, Zongqing Lu, Wenjie Hu, Guohong Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

63 Scopus citations

Abstract

This paper proposes SymDetector, a smartphone based application to unobtrusively detect the sound-related respiratory symptoms occurred in a user's daily life, including sneeze, cough, sniffle and throat clearing. SymDetector uses the builtin microphone on the smartphone to continuously monitor a user's acoustic data and uses multi-level processes to detect and classify the respiratory symptoms. Several practical issues are considered in developing SymDetector, such as users' privacy concerns about their acoustic data, resource constraints of the smartphone and different contexts of the smartphone. We have implemented SymDetector on Galaxy S3 and evaluated its performance in real experiments involving 16 users and 204 days. The experimental results show that SymDetector can detect these four types of respiratory symptoms with high accuracy under various conditions.

Original languageEnglish (US)
Title of host publicationUbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PublisherAssociation for Computing Machinery, Inc
Pages97-108
Number of pages12
ISBN (Electronic)9781450335744
DOIs
StatePublished - Sep 7 2015
Event3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015 - Osaka, Japan
Duration: Sep 7 2015Sep 11 2015

Publication series

NameUbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Other

Other3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015
Country/TerritoryJapan
CityOsaka
Period9/7/159/11/15

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

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