Date of Award

4-2022

Degree Name

Master of Science in Engineering

Department

Electrical and Computer Engineering

First Advisor

Dr. Massood Atashbar

Second Advisor

Dr. Bradley Bazuin

Third Advisor

Dr. Dinesh Maddipatla

Fourth Advisor

Dr. Binu Narakathu

Keywords

Biosignals, blood flow, cardiac metrics, PPG, sensor fusion

Access Setting

Masters Thesis-Open Access

Abstract

Wearable devices with integrated sensors for tracking human vitals are widely used for a variety of applications, including exercise, wellness, and health monitoring. Photoplethysmography (PPG) sensors use pulse oximetry to measure pulse rate, cardiac cycle, oxygen saturation, and blood flow by passing a light beam of variable wavelength through the skin and measuring its reflection. A multi-channel PPG wearable system was developed to include multiple nodes of pulse oximeters, each capable of using different wavelengths of light. The system uses sensor fusion along with a machine learning model to perform feature extraction of relevant cardiovascular metrics across multiple pulse oximeters and predict saturated oxygen (SpO2). The developed model predicted SpO2 with a root mean square (RMSE) of 0.07 and an accuracy of 99.5%. The wearable system was applied to the plant of the foot for vascular assessment. Wearable PPG systems capable of sensor fusion demonstrate a potential capability for continuous evaluation/monitoring of wounds and diseases associated with abnormal blood flow.

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