Identification of gait phases with neural networks for smooth transparent control of a lower limb exoskeleton

Lower limbs exoskeletons provide assistance during standing, squatting, and walking. Gait dynamics, in particular, implies a change in the configuration of the device in terms of contact points, actuation, and system dynamics in general. In order to provide a comfortable experience and maximize performance, the exoskeleton should be controlled smoothly and in a transparent way, which means respectively, minimizing the interaction forces with the user and jerky behavior due to transitions between different configurations. A previous study showed that a smooth control of the exoskeleton can be achieved using a gait phase segmentation based on joint kinematics. Such a segmentation system can be implemented as linear regression and should be personalized for the user after a calibration procedure. In this work, a nonlinear segmentation function based on neural networks is implemented and compared with linear regression. An on-line implementation is then proposed and tested with a subject

Gait Phases Blended Control for Enhancing Transparency on Lower-Limb Exoskeletons

A major challenge in the design and control of exoskeletons is the preservation of the user.s natural behavior when interacting with these machines. From this point of view, one of the most important features is the transparency of the exoskeleton. An ideally transparent exoskeleton follows the user's movements without interaction forces. This is the goal of many control algorithms proposed in the literature. Traditional algorithms are based on finite state machines and are affected by assistive torque discontinuity problems in the transitions between phases. State-of-the-art methods approach this problem by imposing a smooth transition that does not account for variable walking speed. In this work, the authors propose an innovative control algorithm for a lower-limb exoskeleton. The proposed control aims at solving the torque discontinuity problem, without requiring a smooth transitions strategy. The proposed control algorithm continuously blends the output of two independent single stance dynamic models, by weighting the contribution of each stance model to the total assistance based on the gait phase. A linear regressor is used to produce the weights, and it requires a brief user-specific calibration. Results showed a significant reduction of interaction forces, and a longer stride length when compared to two finite-state-machine-based controls at two speeds on the treadmill and one self-selected-speed in an overground walk