Collaborative Research: User-Centered Engineering of a Personalized Exergaming Platform for Pediatric Neurologic Injury

Exergaming lets people use physical or occupational therapy exercises to control video games. It can help children with motor impairments practice therapy exercises at home. Exergames developed by researchers lack the social connections of popular games and can struggle to keep children engaged. This project will develop an exergame system that allows children to control popular video games using upper-body therapy exercises customized to each child. The project will develop an exergaming system that maps therapy exercises to popular video games. New machine learning models and algorithms will be created to recognize exercises customized to a child’s goals and abilities. Overall, the project will result in smart, AI-based strategies to enhance motivation for rehabilitation exercises and improve access to everyday activities. This project will explore how to map therapy exercises for children with neurologic injuries to commercially available video games. This project will create novel exercise recognition algorithms and data-driven models that generalize quickly to new exercise movements. New movement quality measures will be to link features of a child’s movement to how therapists describe movement quality. User-centered engineering criteria and a prototype will be created and tested. The scientific findings will advance knowledge of movement recognition and automatic movement quality metrics for children with motor impairments. This project will also provide insight into how digital health technologies can motivate children and families to do in-home therapy. Beyond rehabilitation, this work will contribute new modeling methods to recognize human movement from small and variable datasets that can be applied to gesture recognition in virtual or augmented reality. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2536211 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Momona Yamagami | Institution: William Marsh Rice University, HOUSTON, TX | Award Amount: $509,756 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2536211 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2536211.html