Indoor sneezing and coughing detection based on COTS wireless device

Abstract

Coughing and sneezing are important routes of virus transmission. Droplets carrying the virus enter the air and spread rapidly, increasing the spread of the disease. Therefore, how to accurately detect coughing and sneezing behaviors in a timely manner so as to effectively warn the spread of the virus has become an urgent problem. To solve this problem, we designs a coughing and sneezing detection scheme for indoor people on commercial wireless devices. First, the Doppler shift feature image caused by the action is segmented using a clustering algorithm, which reduces the computational overhead of the system. Then, the HOG features of the segmented image are extracted and input to the two-dimensional SOM network for action classification and recognition, which effectively improves the detection accuracy of target actions. Finally, a dataset consisting of real coughing and sneezing actions is constructed and open-sourced in this paper. The performance of this solution was tested and analyzed in several dimensions under two typical application scenarios. The results show the robustness of this scheme and the accuracy up to 93.1% in real-world scenarios. Our solution offers a new technology and method for disease prevention detection.