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Theses and Dissertations

Methods for Extraction of Physiological Signals from Wrist-Worn PPG Sensor and Doppler Radar --*DATE CHANGE*


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Date:  Tue, May 10, 2022
Time:  2:30pm - 4:00pm
Location:  Holmes Hall 389
Speaker:  Grant Stankaitis, candidate for MS, advisor: Dr. Olga Boric-Lubecke

Note this was originally scheduled for May 9, recently changed to May 10

Abstract 

This research presents methods developed to extract physiological signals from subjects using both contact and non-contact devices. This research covers the tools used to extract physiological signals and applications for these tools. A waveform analysis will be performed to compare the signals recorded and discuss the physical phenomena associated with the alignment of the signals. Three tools will be presented to extract physiological signals: a wrist-worn photoplethysmography (PPG) sensor, Doppler radar, and wireless data transfer. A PPG sensor in a watch-like formfactor is used to detect physiological signals such as pulse and respiration at the wrist of a subject. Doppler radar is used to detect physiological signals such as respiration from the physical rising and falling movement of the chest. Wireless data transfer is implemented to transfer and process Doppler radar samples in real-time. Two applications for the methods developed will then be presented: remote patient monitoring and smart building applications. Doppler radar physiological sensing coupled with sensing using a wrist-worn PPG sensor in a watch-like form-factor allows a patient to have access to comfortable contact and non-contact options that can be used to monitor vital signs. Additionally, Doppler radar-based occupancy sensors are currently under development for accurate building monitoring systems. With wireless connectivity implemented, Doppler radar occupancy sensor data is processed in real-time and uploaded to a user interface to display occupancy statistics of a given room or building over time.

Bio

Grant Stankaitis is an MS candidate in Electrical Engineering at the University of Hawaiʻi at Mānoa. He received his BS in Computer Science from Chapman University. His current research interests focus on contact and non-contact sensing using wearable devices and Doppler radar and the applications of wireless sensing, such as remote patient monitoring or smart building applications. Outside of his research, Grant enjoys working on projects related to wearables, embedded systems, and IoT devices.

Online available, register for connection info at https://forms.gle/yeGtuLSFYqgbEJg86


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