Adaptive Parameter-Based Human Respiratory Detection System Using Fresnel Zone

Noncontact detection of human respiration has attracted more and more attention due to its advantages of comfort and safety. The detection system using Fresnel model is one of the most promising schemes in recent years. However, most of the existing respiratory detection systems have fixed parameters, such as the respiratory detection system based on commercial WiFi. They inevitably have detection blind areas, which greatly limits the practical application scenarios. The adaptive parameter system can eliminate detection blind areas by adjusting system parameters flexibly. But there are few studies on adaptive parameter adjusting. In this article, we first establish the mathematical relationship between system parameters and respiratory detection effect, which help the system adjust parameters when human position is known. Furthermore, we propose a method based on statistical ideas to measure the respiratory detection capability of system, which gives theoretical basis for parameters’ adjustment of the adaptive parameter system when human position is unknown. Finally, we design a verification system based on universal software radio peripheral (USRP) equipment to prove our method. Experiment shows that the respiratory detection rate of the adaptive parameter system to the human with random position reaches 92 %, while that of the fixed parameter system is only 76 %. The adaptive parameter system can be used for elderly respiratory monitoring and postdisaster rescue.