Mobility is a causal factor in development. Childrenwith mobility impairments may rely upon power mobility for independenceand thus require advanced driving skills to function independently.Our previous studies show that while infants can learnto drive directly to a goal using conventional joysticks in severalmonths of training, they are unable in this timeframe to acquire theadvanced skill to avoid obstacles while driving. Without adequatedriving training, children are unable to explore the environmentsafely, the consequences of which may in turn increase their risk fordevelopmental delay. The goal of this research therefore is to trainchildren seated on mobile robots to purposefully and safely driveindoors. In this paper, we present results where ten typically-developingtoddlers are trained to drive a robot within an obstaclecourse. We also report a case study with a toddler with spina-bifidawho cannot independently walk. Using algorithms based onartificial potential fields to avoid obstacles, we create force field onthe joystick that trains the children to navigate while avoiding obstacles.In this “assist-as-needed” approach, if the child steers thejoystick outside a force tunnel centered on the desired direction,the driver experiences a bias force on the hand. Our results suggestthat the use of a force-feedback joystick may yield faster learningthan the use of a conventional joystick.
|Journal||IEEE Transactions on Neural Systems and Rehabilitation Engineering|
|Publication status||Published - Jun 2011|