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

摘要

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.