Restricted Research - Award List, Note/Discussion Page
Fiscal Year: 2023
1562 The University of Texas at Arlington (143450)
Principal Investigator: Fillia Makedon,makedon@uta.edu,(817) 273-3605
Total Amount of Contract, Award, or Gift (Annual before 2011): $ 218,324
Exceeds $250,000 (Is it flagged?): No
Start and End Dates: 10/1/22 - 9/30/25
Restricted Research: YES
Academic Discipline: Department of Computer Science & Engineering
Department, Center, School, or Institute: none
Title of Contract, Award, or Gift: Collaborative Research: DARE: A Personalized Assistive Robotic System that assesses Cognitive Fatigue in Persons with Paralysis
Name of Granting or Contracting Agency/Entity:
National Science Foundation (NSF)
CFDA Link: NSF
47.041
Program Title:
Disability and Rehabilitation Engineering (DARE)
CFDA Linked: Engineering Grants
Note:
(SAM Category 1.1.1.) With the advancements in robotics and Artificial Intelligence (AI), assistive robotic systems have the potential to provide support and care to people with Spinal Cord Injury (SCI). As robots become more widespread, like today’s mobile phones, assistive robots can play a significant role in assisting persons with disabilities at home, improving independence and everyday quality of life. For example, a robot may assist an individual with motor impairments to perform a task, such as preparing lunch. Current research focuses on ensuring safe Human-Robot Cooperation (HRC) in industrial environments, which will not threaten or harm the physical health of the human teammate. However, there is limited research on understanding the cognitive or mental state of a human who cooperates with a robot daily. Our focus is on enabling assistive robots to assess the Cognitive Fatigue (CF) level the human teammate may have. The objective of this project is to design and develop an end-to-end Personalized Assistive Robotic System, called iRCSA (Intelligent Robotic Cooperation for Safe Assistance), to recognize, assess, and respond to a human’s CF during an HRC task. To achieve this, we will develop a multi-sensory system that collects physiological data from the human teammate during an HRC task and then applies advanced Machine Learning (ML)/Deep Learning (DL) methods to extract key features that are used to automatically assess the level of the human’s CF. Based on the CF assessment, the iRCSA system will adapt the robot’s behavior in order to provide personalized support. We will develop HRC scenarios where a human suffering from SCI and a robot cooperate during daily tasks, e.g. cooking, getting ready to go out, and other tasks. For the design, development, and evaluation of iRCSA, we will follow the Participatory Action Research (PAR) approach by involving students suffering from SCI in every stage of the project. Their valuable insight and feedback will be crucial to ensuring the acceptability and usability of the proposed system.
Discussion: No discussion notes