Detection and measurement of human motion and respiration with microwave Doppler sensor

A microwave Doppler sensor can monitor human motion without contact. It can sense wide range of motion from minute oscillation like respiration to large movement like walking because it measures the distance change between the target and the sensor as signal phase change. However, the proper method for the signal phase estimation is different between motion and respiration measurement. In this paper, we compare three methods for signal phase estimation and find the optimal method and parameters for each problem. For switching to the proper phase estimation method or monitoring human state, it is important to detect motion signal and respiration signal from the raw signal of sensor output. Three kinds of features, energy, frequency-domain entropy and histogram are extracted and are input into binary classifiers. We tested least squares, SVM and AdaBoost classifiers.