Accelerating motor adaptation by influencing neural computations.

When people learn to reach or step in a novel dynamic environment, they initially exhibit a large trajectory error, which they gradually reduce with practice. The error evolution is well modeled by a process in which the motor command on the next movement is adjusted in proportion to the previous movement's trajectory error. We hypothesized that we could accelerate motor adaptation by transiently increasing trajectory error. We tested this hypothesis by quantifying adaptation to a viscous force field applied during the swing phase of stepping in two conditions. In the first condition, we applied then removed the field for 75 steps each, for four iterations. Subjects adapted to each field exposure with a mean time constant of 3.4 steps. In the second condition, we repeated this experiment, but increased the strength of the field for only the first step in each field exposure. We predicted the field strength increase needed by solving a finite difference equation that described the error evolution. Adaptation was significantly faster when the field was transiently amplified (mean time constant = 2 trials). These results demonstrate that it is possible to increase the rate of adaptation to a novel dynamic environment based on knowledge of the computational mechanisms that underlie adaptation.

Robotic gait training: toward more natural movements and optimal training algorithms.

This paper overviews our recent efforts to develop robotic devices to help people relearn how to walk after spinal cord injury. Our efforts are focused on two goals. The first is to develop robotic devices that allow natural gait movements and good force control. We have developed a five degrees-of-freedom robot (PAM) that accommodates natural pelvic movement during walking. PAM uses pneumatic actuators and a nonlinear control algorithm to achieve good force control. We have also developed a novel leg robot, ARTHuR, which makes use of a linear motor to precisely apply forces to the leg during stepping. Our second goal is to develop optimal training algorithms for robotic gait training. Toward this goal, we have developed a small-scale robotic device that allows us to test locomotor training techniques in rodent models. We have also developed an instrumentation system that allows us to measure how experienced therapists manually assist limb movement. Finally, we are developing computational models of motor rehabilitation. These models suggest that assisting in stepping only as needed with a force-controlled robotic device may be an effective method for improving locomotor recovery.