Ph.D. Dissertation Proposal Defense Bridging the Gap between Atomic and Complex Activities in First Person Video using Fuzzy Inference By Bradley Schneider

Webex Meeting Link:

https://wright.webex.com/wright/j.php?MTID=m2a6a03dcbbc75aeb5a275da6d8fa1560

Ph.D. Committee:  Drs. Tanvi Banerjee (advisor), Yong Pei, Thomas Wischgoll, Mateen Rizki, and Michael Riley (University of Cincinnati)

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 ADL'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 enhance explainability and interpretability of results over popular machine-learning classifiers.