Human Respiratory Waveform Sensing Algorithm Based on WiFi Channel State Information

The purpose of this article is to explore the human respiratory waveform sensing algorithm based on WiFi channel state information (CSI) to realize non-contact human respiratory monitoring. In the method, the CSI value is analyzed by using LinuxCSI-Tool tool, and the data is processed and analyzed by using MATLAB software. By comparing the dynamic component intensity (DCI) and respiratory signal-to-noise ratio under different parameters K, the algorithm parameters are optimized and the sensing performance is improved. When the parameter k is set to 3, the algorithm can extract the CSI changes caused by respiratory activities most effectively and realize the accurate perception of respiratory waveform. To sum up, the human respiratory waveform sensing algorithm based on WiFi CSI proposed in this article has high practicability. It provides a new method of non-contact respiratory monitoring for medical and health fields, and also provides a new idea for applications in smart homes and other fields. In the future, with the continuous progress of technology and the in-depth expansion of application, this technology is expected to play an important role in more fields and bring more convenience to people's lives.