Guan, Quan2024-06-052024-06-052024-03https://hdl.handle.net/11299/263678University of Minnesota M.S. thesis. March 2024. Major: Electrical/Computer Engineering. Advisor: John Sartori. 1 computer file (PDF); xi, 52 pages.More than 67 million Americans -- about 20% of the U.S. population -- suffer from chronic pain. In addition to the suffering that it induces, chronic pain also leads to higher prevalence of depression, insomnia, and physical and mental impairment. A study showed the estimated cost for chronic pain management was between $560-635 billion in 2010 dollars. Despite the significant problems that chronic pain presents, there is a lack of effective pain management solutions. Medication-based treatments are not completely effective, can lead to drug overdoses, and play a major role in the current opioid crisis. Mind and body interventions (MBIs) -- activities that change mental and physical state -- have been shown to be an effective pain management solution without the negative side effects associated with typical approaches. One particular MBI is using a scent to help patients develop and maintain a mental association between the scent and a lower pain state that can later be used to manage or prevent a pain episode. This approach relies on an automated pain assessment and scent delivery device that can accompany a patient wherever they go. The most logical implementation involves a wearable device to monitor physiological signals related to pain and dispense scents as necessary. In this work, we describe a wearable device that monitors various physiological signals and processes them to predict a user's pain state. Various machine-learning models are trained with the data collected from clinical trials using a prototype of the wearable device. The correlation between physiological signals and pain agrees with results from literature, but the performance of machine learning models is not ideal due to limited dataset size. Results suggest that collection of additional physiological data for model training will increase performance. This work represents an important step toward novel, safe, and effective MBI-based chronic pain management.enMachin LearningPain AssessmentWearableThesis or Dissertation