A Real-Time Respiratory Pattern Classification System Based on Edge Computing for 60 Ghz mmWave Radar

Millimeter wave radar can be used for monitoring vital signs of personnel under non-contact conditions, and has stronger anti-interference and privacy capabilities. This article proposes a method based on the 60GHz FMCW millimeter wave radar system, which utilizes an improved ResNet50 network to classify six respiratory patterns of the human body (Eupnea, Tachypnea, Bradypnea, Biots, Cheyne Stokes, and Central Apnea) based on the echo waveform. This method demonstrates high accuracy and robustness in respiratory classification in complex environments; In addition, we also use the network reasoning optimization method to deploy it on edge computing devices; According to the experiment, when the refresh rate of edge computing system is 20Hz, the recognition accuracy of this method is up to 95.25%, which verifies the accuracy and robustness of this method.