Paper Info
Title
Automated Time Synchronization of Cough Events from Multimodal Sensors in Mobile Devices
Abstract
Tracking the type and frequency of cough events is critical for monitoring respiratory diseases. Coughs are one of the most common symptoms of respiratory and infectious diseases like COVID-19, and a cough monitoring system could have been vital in remote monitoring during a pandemic like COVID-19. While the existing solutions for cough monitoring use unimodal (e.g., audio) approaches for detecting coughs, a fusion of multimodal sensors (e.g., audio and accelerometer) from multiple devices (e.g., phone and watch) are likely to discover additional insights and can help to track the exacerbation of the respiratory conditions. However, such multimodal and multidevice fusion requires accurate time synchronization, which could be challenging for coughs as coughs are usually concise events (0.3-0.7 seconds). In this paper, we first demonstrate the time synchronization challenges of cough synchronization based on the cough data collected from two studies. Then we highlight the performance of a cross-correlation based time synchronization algorithm on the alignment of cough events. Our algorithm can synchronize 98.9% of cough events with an average synchronization error of 0.046s from two devices.
Year
DOI
Venue
2020
10.1145/3382507.3418855
ICMI '20: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION Virtual Event Netherlands October, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7581-8
1
PageRank 
References 
Authors
0.35
0
10
Name
Order
Citations
PageRank
Tousif Ahmed1326.26
Mohsin Y. Ahmed210.35
Md. Mahmudur Rahman31716.00
Ebrahim Nemati48415.30
Bashima Islam5152.54
Korosh Vatanparvar613416.20
Viswam Nathan75014.09
Daniel McCaffrey810.69
Jilong Kuang93817.00
Jun Alex Gao1022.42