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|Title:||Toward a wearable crowdsource system to monitor respiratory symptoms for pandemic early warning||Authors:||Zhang, Wei
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2021||Source:||Zhang, W., Li, J., Wen, Y. & Luo, Y. (2021). Toward a wearable crowdsource system to monitor respiratory symptoms for pandemic early warning. IEEE Network, 35(3), 56-63. https://dx.doi.org/10.1109/MNET.011.2000718||Project:||NRF2015ENC_GBICRD001-012
|Journal:||IEEE Network||Abstract:||Early warning of a potential pandemic with res- piratory symptoms is crucial for global health management. It enables timely intervention to reduce the likelihood of uncon- trollable massive virus spread. In this research, we propose to leverage the ubiquitous wearable devices to develop a wearable crowdsource system to monitor respiratory symptoms such as cough and fever. Wearable devices nowadays can directly and non-intrusively measure people’s vital signs in real-time with a variety of sensors embedded. We collect the data from wearable devices and develop machine learning algorithms to analyze the data for respiratory symptom monitoring and early warning. In particular, we focus on cough detection through multi-source data fusion (e.g., accelerometer amplitude and microphone audio). Preliminary results show that our algorithms result in higher detection accuracy and less false positive with the least use of computing resources. This research potentially transforms the way the pandemic early warning is implemented and the way we respond to public health crises in the years to come.||URI:||https://hdl.handle.net/10356/152737||ISSN:||0890-8044||DOI:||10.1109/MNET.011.2000718||Rights:||© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/MNET.011.2000718.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
Updated on May 15, 2022
Updated on May 15, 2022
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