Abstract-Locomotor training using body weight support on a treadmill and manual assistance is a promising rehabilitation technique following neurological injuries, such as spinal cord injury (SCI) and stroke. Previous robots that automate this technique impose constraints on naturalistic walking due to their kinematic structure, and are typically operated in a stiff mode, limiting the ability of the patient or human trainer to influence the stepping pattern. We developed a pneumatic gait training robot that allows for a full range of natural motion of the legs and pelvis during treadmill walking, and provides compliant assistance. However, we observed an unexpected consequence of the device's compliance: unimpaired and SCI individuals invariably began walking out-ofphase with the device. Thus, the robot perturbed rather than assisted stepping. To address this problem, we developed a novel algorithm that synchronizes the device in real-time to the actual motion of the individual by sensing the state error and adjusting the replay timing to reduce this error. This paper describes data from experiments with individuals with SCI that demonstrate the effectiveness of the synchronization algorithm, and the potential of the device for relieving the trainers of strenuous work while maintaining naturalistic stepping.

Abstract-This article reviews several tools we have developed to improve the understanding of locomotor training following spinal cord injury (SCI), with a view toward implementing locomotor training with robotic devices. We have developed (1) a small-scale robotic device that allows testing of locomotor training techniques in rodent models, (2) an instrumentation system that measures the forces and motions used by experienced human therapists as they manually assist leg movement during locomotor training, (3) a powerful, lightweight leg robot that allows investigation of motor adaptation during stepping in response to force-field perturbations, and (4) computational models for locomotor training. Results from the initial use of these tools suggest that an optimal gait-training robot will minimize disruptive sensory input, facilitate appropriate sensory input and gait mechanics, and intelligently grade and time its assistance. Currently, we are developing a pneumatic robot designed to meet these specifications as it assists leg and pelvic motion of people with SCI.

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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.