Human respiratory feature extraction on an UWB radar signal processing platform

This paper presents a human respiratory feature extraction algorithm and its implementation on an ultra-wideband (UWB) impulse-radio radar signal processing platform. The conventional human detection algorithms only extract the respiration rate by the radar system. However, there is more information that is never explored in the radar-detected respiratory signals. Thus, this study proposes a modified raised cosine waveform as the respiration model and an iterative feature extraction algorithm to acquire more respiratory features, such as inspiration and expiration speeds, respiration intensity, and respiration holding ratio. These extracted features can be regarded as the compressed signals for the long-term remote medical monitoring system. The proposed respiratory feature extraction algorithm is designed and implemented on a radar signal processing platform with an Radar front-end chip, an ARM processor, and an FPGA chip. The proposed circuit can detect human respiratory signals from 0.1 to 1 Hz rate and analyze the respiratory features for each period of the respiratory signal.