Early warning of a potential pandemic with respiratory symptoms is crucial for global health management. It enables timely intervention to reduce the likelihood of uncontrollable 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 positives with the lowest use of computing resources. This research potentially transforms the way pandemic early warning is implemented and the way we respond to public health crises in the years to come.