Parametric generation of three-dimensional gait for robot-assisted rehabilitation.

For robot-assisted rehabilitation and assessment of patients with motor dysfunction, the parametric generation of their normal gait as the input for the robot is essential to match with the features of the patient to a greater extent. In addition, the gait needs to be in three-dimensional space, which meets the physiological structure of the human better, rather than only on a sagittal plane. Thus, a method for the parametric generation of three-dimensional gait based on the influence of the motion parameters and structure parameters is presented. First, the three-dimensional gait kinematic of participants is collected, and trajectories of ankle joint angle and ankle center position are calculated. Second, for the trajectories, gait features are extracted including gait events indicating the physiological features of walking gait, in addition to extremes indicating the geometrical features of the trajectories. Third, regression models are derived after using leave-one-out cross-validation for model optimization. Finally, cubic splines are fitted between the predicted gait features to generate the trajectories for a full gait cycle. It is inferred that the generated curves match the measured curves well. The method presented herein gives an important reference for research into lower limb rehabilitation robots.