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Description
While pulse oximeter technology is not necessarily an area of new technology, advancements in performance and package of pulse sensors have been opening up the opportunities to use these sensors in locations other than the traditional finger monitoring location. This research report examines the full potential of creating a

While pulse oximeter technology is not necessarily an area of new technology, advancements in performance and package of pulse sensors have been opening up the opportunities to use these sensors in locations other than the traditional finger monitoring location. This research report examines the full potential of creating a minimally invasive physiological and environmental observance method from the ear location. With the use of a pulse oximeter and accelerometer located within the ear, there is the opportunity to provide a more in-depth means to monitor a pilot for a Gravity-Induced Loss of Consciousness (GLOC) scenario while not adding any new restriction to the pilot's movement while in flight. Additionally, building from the GLOC scenario system, other safety monitoring systems for military and first responders are explored by alternating the physiological and environmental sensors. This work presents the design and development of hardware, signal processing algorithms, prototype development, and testing results of an in-ear wearable physiological sensor.
ContributorsNichols, Kevin (Author) / Redkar, Sangram (Thesis advisor) / Tripp Jr., Llyod (Committee member) / Dwivedi, Prabha (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2021
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Description
Photoplethysmography (PPG) is currently a leading and growing field of researchwithin the biomedical industry. With its primary use in pulse oximetry and capability of quickly, non-intrusively, evaluating essential vital signs like heart rate and oxygen levels. This thesis will explore the literature on new and innovative research in pulse oximetry. Then introduce PPG

Photoplethysmography (PPG) is currently a leading and growing field of researchwithin the biomedical industry. With its primary use in pulse oximetry and capability of quickly, non-intrusively, evaluating essential vital signs like heart rate and oxygen levels. This thesis will explore the literature on new and innovative research in pulse oximetry. Then introduce PPG signals including how to calculate heart rate, oxygen saturation, and current problems, mainly focused on motion artifacts. The development of hardware and software systems using Bluetooth to transmit data to MATLAB for algorithm processing. Testing different signal processing techniques and parameters evaluating their effects on algorithm accuracy and reduction of motion artifact. Using accelerometers to identify motion and apply filters to effectively reduce minor motion artifacts. Then perform real-time data analysis and algorithm processing resulting in heart rate and oxygen level calculations.
ContributorsMuhn, George (Author) / Redkar, Sangram (Thesis advisor) / Nichols, Kevin (Committee member) / Subramanian, Susheelkumar (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Photoplethysmography (PPG) is a noninvasive optical signal that measures the change in blood volume. This particular signal can be interpreted to yield heart rate (HR) information which is commonly used in medical settings and diagnostics through wearable devices. The noninvasive nature of the measurement of the signal however causes it

Photoplethysmography (PPG) is a noninvasive optical signal that measures the change in blood volume. This particular signal can be interpreted to yield heart rate (HR) information which is commonly used in medical settings and diagnostics through wearable devices. The noninvasive nature of the measurement of the signal however causes it to be susceptible to noise sources such as motion artifacts (MA). This research starts by describing an end-to-end embedded HR estimation system that leverages noisy PPG and accelerometer data through machine learning (ML) to estimate HR. Through embedded ML for HR estimation, the limitations and challenges are highlighted, and a different HR estimation method is proposed. Next, a point-based value iteration (PBVI) framework is proposed to optimally select HR estimation filters based on the observed user activity. Lastly, the underlying dynamics of the PPG are explored in order to create a sparse dynamic expression of the PPG signal, which can be used to simulate PPG data to improve ML or remove MA from PPG.
ContributorsSindorf, Jacob (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Phatak, Amar (Committee member) / Arizona State University (Publisher)
Created2023