The investigation of non-contact vital signs detection microwave theoretical models and smart sensing systems : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Department of Mechanical and Electrical Engineering, SF&AT at Massey University, Palmerston North, New Zealand
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Date
2020
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Massey University
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Abstract
Natural disasters, such as floods, landslides and earthquakes, occur frequently around the world. The consequences of such disasters in developing countries tend to be more severe due to the lack of effective life detector systems. Life signs detecting has been an active and challenging research field that has great potential in the applications such as finding human lives under debris and non-invasive diagnosis and health monitoring. There are obvious limitations of conventional devices such as optical or acoustic detectors. The optical equipment requires operation from experts, while the acoustics need a quiet environment. The detectors with the thermal sensors and wireless tracking systems are also insufficient when the "non-line of sight" problem appears. In addition, vital signs information (such as heartbeat and breathing rate) from non-invasive microwave sensors are very important to locate people or predict health conditions in the cases of defense, smart home applications, and baby monitoring. Since NASA proposed the use of microwave radar sensing system for life detecting, research and implementation on sensitive, effective, and economic vital signs sensing systems based on microwave signals have become very active. Until now, most research on life detectors has concentrated on hardware development, signal processing, and development of new algorithms to improve accuracy of vital signs detection.
The present study has focused on microwave sensors, studying microwave theoretical models and searching for life detecting, health care and smart home applications. In this research, the antennae systems for vital signs detection, such as breathing rate, were first investigated to validate their performance in a system at different frequencies. The antennae system had an extremely large band width, operating from L band to the X band. Based on the proposed antennae system, models to evaluate the false alarm/detection probabilities of a microwave sensing system were then developed and validated to examine the accuracy of the system in advance. These models are very useful for hardware development of microwave radar sensors. Further investigation into the theoretical models, proposed a novel system that was inspired by the micro bat animal's physical structure. This system showed an enhancement in the accuracy and directional signals of the microwave sensing system. Artificial intelligence was then integrated with the radar sensing system to develop the smart microwave radar sensing system. The machine learning/ deep learning models based on the collected data were developed. The study indicated high accuracy in classifying different types of breathing disorders.
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Listed in 2020 Dean's List of Exceptional Theses
Some Figures have been re-used with permission or under a Creative Commons licence. The following Figures, however, were removed for copyright reasons: Figs 2.6 (=Li et al., 2009 Fig 5); 2.9 & 2.10 (=Chen et al., 2000 Figs 1 & 3); 2.11 (=JalaliBidgoli et al., 2015 Fig 1); 2.15 (=Wang et al., 2014 Fig 1); 2.16 & 2.17 (=Liu & Liu, 2014 Figs 2 & 11); 2.19 & 2.20 (=Lai & Narayanan, 2010 Figs 1 & 11).
Some Figures have been re-used with permission or under a Creative Commons licence. The following Figures, however, were removed for copyright reasons: Figs 2.6 (=Li et al., 2009 Fig 5); 2.9 & 2.10 (=Chen et al., 2000 Figs 1 & 3); 2.11 (=JalaliBidgoli et al., 2015 Fig 1); 2.15 (=Wang et al., 2014 Fig 1); 2.16 & 2.17 (=Liu & Liu, 2014 Figs 2 & 11); 2.19 & 2.20 (=Lai & Narayanan, 2010 Figs 1 & 11).
Keywords
Microwave detectors, Mathematical models, Microwave antennas, Design and construction, Vital signs, Measurement, Doppler radar, Artificial intelligence, Dean's List of Exceptional Theses