Neurofeedback Scheduling in Skill Learning

Biofeedback technologies readily provide massive amounts of biometric data to individuals, but less is known about how frequently such feedback should be delivered for optimal learning or health outcomes. In this internally funded work in collaboration with i-BrainTech, we are investigating the impact of different neurofeedback schedules on skill learning in i-Braintech's brain-computer interface (BCI).

Movement-Assistive Robotics in Motor Learning

Movement-assistive robotics such as exoskeletons hold great promise for rehabilitation contexts, but they have primarily been studied with walking, which can be categorized as a highly complex movement skill that most users are already skilled at. In this NSF-funded collaborative work with FAMU-FSU College of Engineering (Dr. Taylor Higgins) and Georgia Tech (Dr. Shreyas Kousik), we simplify the problem space by studying how intelligent movement-assistive robotics can be used to improve the learning of a unicycle (a balance-related skill like walking, but few people are skilled at).

Watch a short clip ↗

Measuring Technological Savviness

Given the rapid advancement of intelligent technologies in modern society, the ability to quickly learn new technologies (“tech-savviness”) is growing into an essential 21st-century skill. In this work funded by DEVCOM ARL in collaboration with Florida IHMC, we study which behaviors are adaptive in learning new digital platforms, and which previous technology-related experiences and dispositions make someone more likely to be tech-savvy.