Contactless Respiration Monitoring Using Wi-Fi and Artificial Neural Network Detection Method

Detecting respiration in a non-intrusive manner is beneficial not only for convenience but also for cases where the traditional ways cannot be applied. This paper presents a novel simple low-cost system where ambient Wi-Fi signals are acquired by a third-party tool (Nexmon) installed in a Raspberry Pi and is able to detect the respiration time domain waveform of a person. This tool was selected as it uses 80 MHz bandwidth of the Wi-Fi signal and supports the latest implementations that are widely used, such as 802.11ac. A neural network is developed to detect the respiration frequency of the waveform. Generated waves emulating respiration waveforms were used for training, validating, and testing the model. The model can be applied to unseen real measurement data and successfully determine the breathing frequency with a very low average error of 4.7% tested in 20 measurement datasets.