Pulse oximetry is a non-invasive technique for measuring the amount of oxygen in a patient's blood (SpO2). It is considered standard of care in the hospital for monitoring cardio-respiratory function. While it has potential uses in ambulatory or wearable applications, pulse oximetry is susceptible to motion artifact contamination. This thesis presents efforts to quantify and model the effects of motion artifact, and automatically detect periods of poor signal quality. First, the effects of motion artifact on SpO2 are analyzed using motion contaminated data. Second, two models are identified from previous literature that may explain the effects of motion artifact. These models are developed analytically and evaluated using isolated motion artifact signals. Finally, three signal quality assessment algorithms are proposed. These algorithms are shown to discriminate between clean and contaminated signals. This thesis attempts to inform the development of techniques to mitigate the effects of poor signal quality on pulse oximetry.