UWB Simultaneous Breathing and Heart Rate Detections in Driving Scenario Using Multi-Feature Alignment Two-Layer EEMD Method

• Published in: IEEE sensors journal Vol. 20; no. 17; pp. 10251 - 10266
• Main Authors: Shyu, Kuo-Kai, Chiu, Luan-Jiau, Lee, Po-Lei, Lee, Lung-Hao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alignment; Biomedical monitoring; breathing rate; Diagnostic systems; Dielectrics; driver; Drivers; ensemble empirical mode decomposition (EEMD); Feature extraction; Heart beat; Heart rate; Human motion; intrinsic mode function (IMF); Motion perception; Physiology; Remote monitoring; Remote sensing; Sensors; tracking; Ultra wideband radar; ultra-wideband (UWB) radar; Ultrawideband radar; Vehicles
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.2992687

Remote sensing of life detection or a non-contact monitor of vital signals is an important application for Ultra-wideband (UWB) radar, such as health monitoring of a vehicle driver. Using the UWB radar to detect physiological signals of a dynamic human, three kind movement features (body motion, breathing, and heartbeat) must be considered generally to be extracted from echo pulses. Usually, moving body echo signal is much larger than other twos, which will cause signal interference and interaction problems. Meanwhile, since moving body causes breathing and heartbeat to act in dynamic spatial position, conventional fixed feature with maximum spectrum or local peak spectrum detection methods are not likely to find those movable physiological active features. Thus, how to reduce physiological feature bias from body motion, and efficiently obtain valuable physiological information are problems. To solve these problems, a novel multi-feature alignment (MFA) two-layer EEMD method is proposed. The proposed method simultaneously detects breathing and heartbeat information from a slightly swinging human (driver). The simulation and experiment results show the proposed method can effectively and reliably evaluate breathing and heart rates in static or/and dynamic body situation, both in laboratory and car.