Planning Grant: I/UCRC for Assistive Technologies to Enhance Human Performance

University Of Texas At Dallas, Richardson TX

Investigators

Abstract

1338930 University of Texas at Arlington (UTA); Makedon 1338932 University of Texas at Dallas (UTD); Daescu The proposed I/UCRC will focus on assistive technologies to enhance human performance by removing or helping to overcome physical, cognitive or operational domain obstacles and help the human reach his or her potential as much as possible. The research efforts will be anchored by the University of Texas at Arlington (UTA) as the lead institution, partnered with the University of Texas at Dallas (UTD). Assistive technologies (AT) use computer tools and methods to increase, maintain, or improve the functional capabilities of people with disabilities, as well as to enhance the productivity of well-bodied people. ATs can improve work efficiency, identify safety risks, shorten the learning curve or worker training through simulations, improve resource allocation, creativity and communication. In healthcare environments, AT tools can enhance sensory and cognitive capabilities, improve training & delivery, enable remote monitoring, delay physical and cognitive decline in chronic conditions, personalize rehabilitation, predict risks for the elderly who live alone, monitor sleep disorders, design better prosthetics or drugs, design better robotic assistants, smart wheelchairs, therapy games and tools to monitor mental/physiological conditions, such as depression, epilepsy, or heart problems. The center will promote the development of AT research infrastructure in industry, research centers and academia. It will help generate new jobs and new types of products and services. The proposed center will help prepare a future generation of competitive employees-scientists, and will provide new opportunities to engage students early, before graduation, in internship or research projects related to the company's interests. Both UTA and UTD, have a strong track record in training students in AT areas and have ongoing research that ranges from better ways to identify software errors, to analysis of facial expressions to identify arthritic pain, or efficient multimodal database searches.