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    High precision accelerometer contact microphones for detection of mechano-acoustic cardiopulmonary signals

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    GUPTA-THESIS-2020.pdf (3.027Mb)
    Date
    2020-07-27
    Author
    Gupta, Pranav
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    Abstract
    The objective of this research is to design, fabricate and characterize a MEMS-based Accelerometer Contact Microphone (ACM) that enables wearable measurement of cardiopulmonary mechano-acoustic signals, providing a platform for longitudinal monitoring of vital health parameters. This work presents a hermetically-sealed low noise, wide bandwidth out-of-plane accelerometer comprising of sub-micron transduction gap (270nm) electrodes implemented using HARPSS+ process, which is fabricated and characterized as a contact microphone. The sensor is designed using a cantilever topology, with a resonant frequency of 13.6kHz and sensitivity of 14.2fF/g. The fabricated sensor is interfaced with a commercial capacitance readout circuit, and a low noise performance with a noise density of 127µg/√Hz and a bias instability of 22µg is measured. A high sensitivity of 76mV/g is recorded by placing the ACM is on a shaker table and applying 1-g sinusoidal acceleration. The ACM is packaged at atmospheric pressure, reducing its operational bandwidth from its measured resonant frequency of 12.5kHz down to 640Hz. The use of the ACM in a body-worn auscultation system is validated by mounting it on the chest, and non-invasively recording acoustic emissions produced within the thoracic cavity. The recorded signals are filtered and processed to extract the phonocardiogram, seismocardiogram, lung sounds, and chest wall motion using data processing techniques, demonstrating the possibility of accurately capturing multiple types of acoustic and vibrational data from the body simultaneously using a single sensor. The efficacy of the ACM is further highlighted by its ability to compute vital health parameters such as heart rate and heart rate variability with accuracy comparable to a medical-grade ECG system.
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    http://hdl.handle.net/1853/63669
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    • Georgia Tech Theses and Dissertations [23877]
    • School of Electrical and Computer Engineering Theses and Dissertations [3381]

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