SymDetector: Detecting Sound-Related Respiratory Symptoms using Smartphones


Publication/Creation Date
September 9 2015
Creators/Contributors
Xiao Sun (creator)
Zongqing Lu (creator)
Wenjie Hu (creator)
Guohunj Cao (creator)
Pennsylvania State University (contributor)
Media Type
Journal Article
Persuasive Intent
Academic
Description
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 built-in 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.
HCI Platform
Carryables
Discursive Type
Inventions
Location on Body
Hand

How to cite this entry
Xiao Sun, Zongqing Lu, Wenjie Hu, Guohunj Cao. (September 9 2015). "SymDetector: Detecting Sound-Related Respiratory Symptoms using Smartphones". 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015). International Joint Conference On Pervasive And Ubiquitous Computing. Fabric of Digital Life. https://fabricofdigitallife.com/index.php/Detail/objects/1234