Applying Genetic Programming To Control Of An Artificial Arm

Robotics researchers at NASA's Johnson Space Center (JSC) and Rice University have made substantial progress in myoelectric teleoperation. A myoelectric teleoperation system translates signals generated by an able-bodied robot operator's muscles during hand motions into commands that drive a robot's hand through identical motions Farry's early work in myoelectric teleoperation used variations over time in the myoelectric spectrum as inputs to neural networks to discriminate grasp types and thumb motions; schemes yielded up to 93% correct classification on thumb motions. More recently, Fernandez achieved 100% correct non-realtime classification of thumb abduction, extension, and flexion on the same myoelectric data using genetic programming to develop functions that discriminate between thumb motions using myoelectric signal parameters. Genetic programming (GP) is an evolutionary programming method where the computer can modify the discriminating functions' form to improve its performance, not just adjust numerical coefficients or weights. While the function development may require much computational time and many training cases, the resulting discrimination functions can run in realtime on modest computers These results suggest that myoelectric signals might be a feasible teleoperation medium, allowing an operator to use his own hand and arm as a master to intuitively control an anthropomorphic robot in a remote location such as outer space. These early results imply that multifunction myoelectric control based on genetic programming is viable for prosthetics, since teleoperation of a robot by an operator with a complete limb is a limiting or "best-case" scenario for myoelectric control We hypothesize that myoelectric signals of traumatic below-elbow amputees can control several movements of a myoelectric hand with the help of a function or functions developed with genetic programming techniques. We are now testing this hypothesis with the help of NASA/ISC under a NASA/JSC - Texas Medical Center Cooperative Grant. In this study, five adult below-elbow amputees are performing two forearm motions, two wrist motions and two grasp motions using their "phantom" limb and sound limb while we collect myoelectric data from four sites on the residual limb and four sites from the sound limb. We will use a variety of myoelectric signal time and frequency features in a genetic programming analysis to evolve functions that discriminate between signals generated during different muscle contractions.