Graduation Semester and Year
Spring 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Science
Department
Computer Science and Engineering
First Advisor
Fillia Makedon
Second Advisor
Christoph Csallner
Third Advisor
Vassilis Athitsos
Fourth Advisor
Nicholas Gans
Abstract
Assistive robotics is a promising area for improving the quality of life of people with paralysis, specifically through assistance in Activities of Daily Living (ADLs). Current state-of-the-art assistive robotic systems do not have the capability to dynamically modulate their functionality according to the cognitive fatigue level of the user, which can negatively impact their effectiveness and usability in real-life settings.
This dissertation explores an adaptive robotic framework that adjusts its behavior depending on the cognitive fatigue level of users. The system operates in three different modes, and switches between Fully Controlled, Semi-Autonomous, and Fully Autonomous modes. The overall goal is to reduce cognitive load while providing personalized assistance during HRC tasks. The system is validated using two real-world tasks - meal preparation and getting ready for work or university. The main contribution of this research is the development of a novel fatigue-aware robotic system that can adapt its behavior based on the cognitive fatigue level of the user and offer personalized assistance when needed.
Keywords
Cognitive Fatigue Detection, Activities of Daily Living (ADL), Human-Robot Interaction, Human-Robot Collaboration, Adaptive Framework, Assistive Robotics, Multimodal Data Analysis, Machine Learning, Deep Learning
Disciplines
Computer Engineering
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Karim, Enamul, "AN INTELLIGENT ROBOTIC SYSTEM FOR MULTI-SENSORY COGNITIVE FATIGUE DETECTION TO ASSIST PERSONS WITH PARALYSIS IN ACTIVITIES OF DAILY LIVING" (2025). Computer Science and Engineering Dissertations. 412.
https://mavmatrix.uta.edu/cse_dissertations/412