This dissertation explores the application of high frequency, low loss, high quality factor thin-film piezoelectric MEMS resonators as passive wireless respiratory monitoring sensors. In this work we build on advances in RF MEMS technology and wearables to develop a respiratory monitoring system capable of measuring respiration without any on-board circuitry and battery First, a wireless MEMS-based respiration sensor that operates without a battery or any on-board circuitry is presented. The sensing system is made up two major components, the wireless sensor and a base transceiver and computational unit. The sensor is only made of up of two RF-MEMS resonators and a custom UHF RFID antenna. The base unit is composed of transmitter antenna connected to a signal generator, and a receiver antenna connected to an oscilloscope. The frequency of the MEMS resonator is highly sensitive towards both temperature and also water vapor condensation. This sensitivity results in the modulation of the resonator (and sensor) when exposed to respiratory airflow. For a mean excitation signal power of 80µW, the sensor was measured to be capable of recording the respiratory profile of human subject from distance of up to 2 meters away from the base transceiver unit. At a distance of 0.5 meters the sensor was measured to have a SNR of 124.8dB. Next, building on the above sensor and using a novel time-of-flight sensing technique a respiratory flow sensor with only 7.2cm2 footprint was developed. To facilitate the time-of-flight sensor, two low loss, high quality factor TPoS MEMS resonators are placed ~1cm apart and are connected to a small (3.8cm2 ) planar ground antenna. We were able to measure flow rate and respiration profile of human subject from a distance of 20cm from the base transceiver.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Electrical and Computer Engineering
Length of Campus-only Access
Doctoral Dissertation (Campus-only Access)
Moradian, Sina, "Development of RF-MEMS Based Passive Wireless Respiratory Monitoring Systems" (2021). Electronic Theses and Dissertations, 2020-. 960.