I/UCRC Phase I: iPerform - I/UCRC for Assistive Technologies to Enhance Human Performance

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The I/UCRC for Assistive Technologies (AT) will attract support for the advancement of AT research and promote innovation in academia that is driven by industrial needs. In work environments, ATs can improve work efficiency, identify safety risks, shorten the learning curve or worker training through simulations, and 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 seed projects conducted within the center will help advance basic research in computer vision, machine learning, user interfaces, brain imaging, human robot interaction, human computer interaction, virtual reality, simulation, and many other related research areas.The projects conducted at I/UCRC for Assistive Technologies will drive a broad spectrum of advances in the areas such as worker productivity and safety, transportation, health, company operations and intelligence, and promote the development of AT research infrastructure. The center addresses real-world problems and thus can generate new jobs, products, services, and impact all areas where a human has the potential to improve. The center will play role in enhancing the quality and diversity of AT professionals and prepare a future generation of competitive employees-scientists who can solve problems due to unmet human needs. Through compelling projects, the center will also attract students to CSE fields. UTA and UTD have a strong record in training students and have ongoing NSF projects, e.g., to identify software errors, analysis of facial expressions to identify arthritic pain, or efficient multimodal database searches.