AUFLIP Daniel Levine, Alan Cheng, Kevin Leonardo, David Olaleye, Matthew Shifrin, Hiroshi Ishii
How can people learn advanced motor skills such as front flips and tennis swings without starting from a young age? The answer, following the work of Masters et. al., we believe, is implicitly. Implicit learning is associated with higher retention and knowledge transfer, but that is unable to be explicitly articulated as a set of rules. To achieve implicit learning is difficult, but may be taught using obscured feedback - that is feedback that does not directly describe the result of an action.
With AUFLIP we wished to provide auditory feedback to help newcomers in learning front flips. We created a wearable system with a simplified model of a front flip that compares a user’s time to peak rotation against their ideal time. As the user approaches their ideal performance, the system begins playing a chord, only completing the chord if the user manages to rotate at their ideal peak time. We tested this system by integrating it into an environment where professional coaches teach novices how to perform front flips, and found preliminary results suggesting that user’s wearing the device exhibited implicit learning.
The front flip has four main parts; takeoff, rise to peak, rotation, and landing. From expert opinion, we found that most newcomers have difficulty rising to peak. We created a simulation of the front flip and we found that we could accurately assess flip performance as ballistic parabolic motion, as the energy mostly comes from the spring floor and is, in the ideal case, is transferred efficiently into the motion by a stiff body.
To implement this simulation + auditory feedback system, we created a wearable sensor band. The AUFLIP sensor band is worn on the ankle. It detects initial takeoff velocity and rotation during flipping, sends the state to a custom phone app which interpolates the chord and sends the user auditory feedback.
AUFLIP Teaching Front Flips with Auditory Feedback Towards a System for Learning Advanced MovementThesis