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A Framework For Mode-Free Prosthetic Control For Unstructured Terrains

Prosthetic limb controllers employ discrete modes for well-defined scenarios such as stair ascent, stair descent, or ramps. General human locomotion, however, is a continuous motion, fluidly adapting to the environment and not always categorizable into modes. It exhibits strong inter-joint coordination and the movement of a single joint can be largely predicted based on the movement of the rest of the body. We show that using body motion from the intact limbs and trunk, a reference trajectory can be generated for a prosthetic joint for every instant in time. Previously we demonstrated that a Recurrent Neural Network (RNN) can predict ankle angle trajectory for structured activities. In this study, we apply a similar network to more unstructured activities which are hard to categorize into modes. A wearable motion capture suit was worn by 10 healthy subjects to record full body kinematics during obstacle avoidance, sidestepping, weaving through cones, and backward walking. We used an RNN to predict right ankle kinematics from the other joint kinematics. The model was robust to subject-specific variations such as walking speed and step length. We present the performance for different activities and using different subsets of the sensors. This system demonstrates the potential for generating a reference trajectory for a prosthesis or other rehabilitation robot without explicit featurization of terrains or gait events.