Toward a wearable crowdsource system to monitor respiratory symptoms for pandemic early warning

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.Nanyang Technological UniversityNational Research Foundation (NRF)Accepted versionThis research is funded by National Research Foundation (NRF) via the Green Buildings Innovation Cluster (Grant NO.: NRF2015ENC_GBICRD001-012), administered by Building and Construction Authority (BCA) Singapore. In addition, this research is sponsored by National Research Foundation (NRF) via the Behavioural Studies in Energy, Water, Waste and Transportation Sectors (Grant NO.: BSEWWT2017_2_06), administered by National University of Singapore (NUS). Moreover, this research is funded by Nanyang Technological University (NTU) via the Data Science & Artificial Intelligence Research Centre @ NTU (Grant NO.: DSAIR@NTU)