Identification of Gait Phases with Neural Networks for Smooth Transparent Control of a Lower Limb Exoskeleton
This work addresses the need for comfortable and high-performance exoskeleton control for users, but it is incremental as it builds on prior linear regression methods.
The paper tackled the problem of controlling a lower limb exoskeleton smoothly and transparently by identifying gait phases, comparing a neural network-based segmentation with linear regression and testing it online with a subject.
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.