Publication Date
2022
Document Type
Dissertation
Committee Members
Tanvi Banerjee, Ph.D. (Advisor); Yong Pei, Ph.D. (Committee Member); Michael Riley, Ph.D. (Committee Member); Mateen Rizki, Ph.D. (Committee Member); Thomas Wischgoll, Ph.D. (Committee Member)
Degree Name
Doctor of Philosophy (PhD)
Abstract
Activities of Daily Living (ADL’s) are the activities that people perform every day in their home as part of their typical routine. The in-home, automated monitoring of ADL’s has broad utility for intelligent systems that enable independent living for the elderly and mentally or physically disabled individuals. With rising interest in electronic health (e-Health) and mobile health (m-Health) technology, opportunities abound for the integration of activity monitoring systems into these newer forms of healthcare. In this dissertation we propose a novel system for describing ’s based on video collected from a wearable camera. Most in-home activities are naturally defined by interaction with objects. We leverage these object-centric activity definitions to develop a set of rules for a Fuzzy Inference System (FIS) that uses video features and the identification of objects to identify and classify activities. Further, we demonstrate that the use of FIS enhances the reliability of the system and provides enhanced explainability and interpretability of results over popular machine-learning classifiers due to the linguistic nature of fuzzy systems.
Page Count
105
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2022
Copyright
Copyright 2022, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
ORCID ID
0000-0003-3059-8288
