Classification of Doppler radar reflections as preprocessing for breathing rate monitoring

Classification is presented as a pre‐processing step in this study. The state of the subject is classified as the unmoving state with normal breathing (normal breathing class), unmoving state with no breathing (stop breathing class) or the state when the subject is moving (erratic signal class) before breathing estimation algorithms are applied. Estimation algorithms may be applied to obtain breathing rate if normal breathing class is detected or alarms may be generated if stop breathing is detected, and fine‐grained classification of activities may be pursued if the erratic signal is detected. Experiments were performed using a single‐channel pulse‐modulated continuous wave radar with three subjects for a total of 135 min. In each experiment, the subject was continuously monitored for 15 min and the subject performed activities that resulted in a signal that belonged to one of the three classes. Besides extracting a feature that assessed the distribution of energy of the signal in the frequency domain, a novel nonlinear time series feature extraction method based on the higher‐dimensional embedding technique was applied to ascertain periodicity of the reflected signal. Bayes classifier was used to classify each 5‐s segment of radar returns. A 30‐fold cross validation resulted in 97% of overall classification accuracy.