| dc.description.abstract | Nowadays, Ultra-wideband (UWB) radar is an important remote sensing tool of life detection or a non-contact monitor of the physiological signals, such as health monitoring for a vehicle driver. By processing the received UWB pulse echoes reflected from the body, different signals corresponding to heart activity and breathing, corrupted by body motion and the environment noise etc., are wanted to be separated clearly for health-monitoring purposes. However, the heartbeat signal is so tiny that it is covered by breathing harmonics and clutters. At the same time, since the frequencies of the vital signals are very close, usually around 1 Hz, it is difficult to apply an ordinary frequency filter to separate them apart. This problem induces that the vital signal detection method, usually, only detects the large breath signal, not the heartbeat signal. Further, the driver body is in static/dynamic situation at any time. Usually, the body motion echo signal is much larger than other twos, which will cause signal interference and interaction problems. Thus, conventional fixed feature detection methods are not likely to find those movable physiological active features. Meanwhile, to reduce physiological feature bias from body motion, and efficiently obtain valuable physiological information are also the subjects worthy of study.
This dissertation proposes a two-layer EEMD method, which effectively eliminates environmental interference and system noise in radar echo signals, and separates human breathing and weak heartbeat signals. Moreover, for static/dynamic human vital detection, the FVPIEF (first valley-peak of IMF energy function) method and the MFA (multi-feature alignment) method are proposed. Both methods utilize the characteristics of the strength attenuation and time delay of UWB radar echo, and the characteristics of conductive media in different tissue layers of the human body to detect the time-region-of-interest (TROI) and extract the single-/multi- feature of vital activities based on local optimization, and calibrate body motion effects. The simulation and experiment results show the proposed methods can effectively and reliably evaluate breathing and heart rates in static or/and dynamic volunteer situation, both in laboratory and car. | en_US |