Yong Pei, Ph.D. (Committee Co-Chair); Nia S. Peters, Ph.D. (Committee Co-Chair); Mateen M. Rizki, Ph.D. (Committee Member)
Master of Science (MS)
Real-time distributed systems constitute computing nodes that are connected by a network and coordinate with one another to accomplish a cooperative task, combining the responsiveness, fault-tolerance and geographic independence to support time-constrained collaborative applications, including distributed Human-Machine Teaming. In this thesis research the viability of real-time distributed collaborative technologies is demonstrated through the design, development and validation of prototype systems that support two human-machine teaming scenarios namely, ACE-IMS (Affirmation Cue based Interruption Management Systems) and ReadMI (Real-time Assessment of Dialogue in Motivational Interview). ACE-IMS demonstrates how a combination of AI capabilities and the cloud and mobile computing infrastructure can be leveraged to extend and improve human-machine collaboration through intelligent interruption dissemination to reduce the potential disruptiveness to the human, while ReadMI demonstrates how AI based technologies like Automatic Speech Recognition and classification algorithms can be augmented into a real-time video conferencing system to enhance user interaction and leverage the computation and decision making capabilities of computers to improve provider fidelity of Motivational Interviewing skills among healthcare professionals.
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
Copyright 2020, some rights reserved. My ETD may be copied and distributed only for non-commercial purposes and may not be modified. All use must give me credit as the original author.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.