Collecting Data Is a Wii Bit of Fun
Why are some people fast learners? Can we teach everybody to be like them? Yes, Wii can.
In a research project recently funded by the National Science Foundation, Rice professors Marcia O’Malley and Michael Byrne are making use of Nintendo’s popular Wii video game technology to codify learning systems in ways that can be used in a range of human endeavors, from sports to surgery.
The project follows up on O’Malley’s pioneering work that utilized robots to map out how people learn physical tasks. The study was used to treat stroke victims, but its ultimate goal was to program robots to teach in new ways.
With the new NSF grant, O’Malley and Byrne will spend the next three years measuring the motions involved in tasks as mundane as playing paddleball and as complex as flying a fighter jet. To do that, having a motion-capture device at hand will be invaluable. The device is called an accelerometer, but video game fans know it as a Wiimote, the handheld wand that serves as a wireless interface between player and screen.
“It’s the only part of the system we really need,” said O’Malley, director of Rice’s Mechatronics and Haptic Interfaces Laboratory. The researchers will compare data from the Wiimote to that from a more expensive Vicon motion capture system to “see how good the Wii really is.”
“We’re already grabbing motion data from the Wiimote.” said O’Malley, “Soon, we’ll be able to measure a range of motion and then turn it into a mathematical model.”
For the researchers, that’s where the games really begin. Their plan is to bring together robotics and virtual reality in a way that lets people absorb information through repetition of the motor pathways. Think of hitting a tennis ball. Learning by trial and error is fine, but it would be much easier if a robotic sleeve could tell you exactly where that hitch in your swing is and gently prod you to hit the ball correctly.
O’Malley and Byrne’s research into what they term the “cognitive modeling of human motor skill acquisition” will focus on three types of learners. “There are experts, who learn at a slow, steady pace, but they get there,” O’Malley said. “There are novices, who learn at a slow, steady pace, but sometimes they never get there. And then there are those who start off awful, but somewhere in the middle of training they suddenly ‘get it.’ We’re interested in how these groups of performers differentiate and if there are inherent characteristics of movement and control policies that lead to expertise.”
Their plan is to bring together robotics and virtual reality in a way that lets people
absorb information through repetition of the motor pathways.
Here’s where Byrne’s own expertise comes in. An associate professor of psychology who specializes in computer–human interaction, he’ll analyze feedback on the range of motion used in performing a task and figure out precisely where the most efficient learning happens.
“I work with the sort of mathematical computational theory of human performance that’s never been extended to the kind of dense motor activity we want to study,” said Byrne. “We find that some Wii games have really good learning properties we can measure, and there also are some that people don’t seem to get a lot better at. I can tell you I’m about as bad at Wii golf now as I was when I started playing it.”