基于WiFi-CSI多载波幅相融合的呼吸监测算法OA

WiFi human respiratory sensing technology has low-cost and non-contact characteristics,which can solve the problems of high cost and poor comfort in wearable respiratory monitoring and thus great development potential.Aiming at the existing respira-tory monitoring technology due to the perception of the"blind spot"which leads to the decrease of respiratory perception range and perception accuracy,a new non-contact respiratory frequency monitoring algorithm WiK-Breath based on WiFi is proposed.K-Means clustering algorithm to cluster 90 subcarriers into several sets with similar characteristics,and control the number of subse-quent subcarriers to be fused by a threshold.Secondly,a respiratory feature sequence was constructed by linearly combining the am-plitude and phase of the subcarriers,and the respiratory frequency results of each subcarrier were weighted and fused to achieve res-piratory frequency monitoring using the BNR values as the weights.Experimental results show that the proposed respiration moni-toring algorithm can achieve a respiration error as low as 3.4%when the human body and the transceiver device are 7 metres apart.Compared with existing algorithms,WiK-Breath's breathing estimation error is reduced by 15.7%.