Powered knee and ankle prostheses can perform a limited number of discrete ambulation tasks. This is largely due to their control architecture, which uses a finite-state machine to select among a set of task-specific controllers. A non-switching controller that supports a continuum of tasks is expected to better facilitate normative biomechanics. This paper introduces a predictive model that represents gait kinematics as a continuous function of gait cycle percentage, speed, and incline. The basis model consists of two parts: basis functions that produce kinematic trajectories over the gait cycle, and task functions that smoothly alter the weight of basis functions in response to task. Kinematic data from ten able-bodied subjects walking at twenty-seven combinations of speed and incline generate training and validation data for this data-driven model. Convex optimization accurately fits the model to experimental data. Automated model order reduction improves predictive abilities by capturing only the most important kinematic changes due to walking tasks. Constraints on range of motion and jerk ensure the safety and comfort of the user. This model produces a smooth continuum of trajectories over task, an impossibility for finite-state control algorithms. Random sub-sampling validation indicates basis modeling predicts untrained kinematics more accurately than linear interpolation.
Previous work has shown that it is possible to use a mechanical phase variable to accurately quantify the progression through a human gait cycle, even in the presence of disturbances. However, mechanical phase variables are highly dependent on the behavior of the body segment from which they are measured, which can change with the human's task or in response to different disturbances. In this study, we compare kinematic parameterization methods based on time, thigh phase angle, and tibia phase angle with motion capture data obtained from ten able-bodied subjects walking at three inclines while experiencing phase-shifting perturbations from a split-belt instrumented treadmill. The belt, direction, and timings of perturbations were quasi-randomly selected to prevent anticipatory action by the subjects and sample different types of perturbations. Statistical analysis revealed that both phase parameterization methods are superior to time parameterization, with thigh phase angle also being superior to tibia phase angle in most cases.
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