Publication Date
2018
Document Type
Thesis
Committee Members
Tanvi Banerjee (Advisor), William Romine (Committee Member), Amit Sheth (Committee Member), Jennifer Hughes (Committee Member), Mateen Rizki (Other)
Degree Name
Master of Science (MS)
Abstract
Dementia caregiver burnout is detrimental to both the familial caregiver and their loved ones with dementia. As the population of older adults increases, both the number of individuals with dementia and their corresponding caregivers increase as well. Thus, we are interested in developing a potential tool to non-invasively detect signs of caregiver burnout using a mobile application combined with machine learning. Hence, the mobile application "Caregiver Assessment using Smart Technology" (CAST) was developed which personalizes a word scramble game. The CAST application utilizes a heuristically constructed Fuzzy Inference System (FIS) optimized via a Genetic Algorithm (GA) to provide an individualized performance measure for each user of CAST. That is, we attempt to adjust the difficulty of the game using an individual user's ability to solve the word scramble tasks. With a cohort of 48 non-caregiver participants and 2 dementia caregiver participants, we report on the construction of the FIS, the optimization of the FIS using the GA, and analysis of the preliminary results of deciding difficulties of words using standard performance metrics precision, recall, and F1 score
Page Count
74
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2018
Copyright
Copyright 2018, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
