S3H: A Symptom Surveillance System in High Spatial Resolution using Smartphones

Abstract

The pandemic outbreaks including seasonal influenza demonstrate the continuous threat from multi-level pandemics and underline the need for robust preparedness and response. The goal of this paper is to develop a symptom surveillance system (S3H), providing supplementary data with public health data for long-term pandemic monitoring and prediction in large population. The system targets at high spatiotemporal monitoring of symptoms in public area including fever and cough distribution. Our preliminary study was conducted in the university library. Thermal imager and sound sensors based on smartphones were used to acquire the raw data. Image processing algorithms were used to calculate the temperature distribution in the certain population. For cough sound recognition, we take the Mel-frequency cepstral (MFC) as the feature of cough sound and kNN algorithm was performed for automatically recognizing the cough sound in a continuous recording.