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Full-Text Articles in Medicine and Health Sciences
Mindfulness And Pain Regulation: The Role Of Acceptance And Commitment Therapy For Individuals With Chronic Pain, Ariana C. White
Mindfulness And Pain Regulation: The Role Of Acceptance And Commitment Therapy For Individuals With Chronic Pain, Ariana C. White
Honors Theses and Capstones
Chronic pain is a significant and widely prevalent health condition which requires comprehensive care to address the many facets contributing to symptomatology. In 2016, 20% of American adults (about 50 million) reported experiencing chronic pain, of which 7.4% indicated that chronic pain frequently limited their life and participation in activities within the past 3 months (CDC, 2018). As a result, many individuals with chronic pain turn to opioid-based medication for pain relief, but long-term use of opioids actually increases pain sensation (Tobin, 2019). Moreover, opioid medication is unable to target underlying mental health components which emerge as part of chronic …
A Review Of Monte Carlo Methods And Their Application In Medical Physics For Simulating Radiation Transport, Joe Shields
A Review Of Monte Carlo Methods And Their Application In Medical Physics For Simulating Radiation Transport, Joe Shields
Honors Theses and Capstones
Monte Carlo methods are used to calculate statistical behavior through the use of random number generators and probability density functions. They have been used extensively in medical physics for research in radiotherapy, designing technology, dosimetry, and advanced clinical applications. This paper provides a background on Monte Carlo methods and a review of radiation therapy physics and dosimetry. Additionally, there is a discussion of the different ways Monte Carlo methods are used in medical physics as well as a review of current research related to Monte Carlo methods. The final portion of this paper contains my own Monte Carlo simulation using …
Realtime Event Detection In Sports Sensor Data With Machine Learning, Mallory Cashman
Realtime Event Detection In Sports Sensor Data With Machine Learning, Mallory Cashman
Honors Theses and Capstones
Machine learning models can be trained to classify time series based sports motion data, without reliance on assumptions about the capabilities of the users or sensors. This can be applied to predict the count of occurrences of an event in a time period. The experiment for this research uses lacrosse data, collected in partnership with SPAITR - a UNH undergraduate startup developing motion tracking devices for lacrosse. Decision Tree and Support Vector Machine (SVM) models are trained and perform with high success rates. These models improve upon previous work in human motion event detection and can be used a reference …