Synthesis of a six-bar mechanism for generating knee and ankle motion trajectories using deep generative neural network

Abstract

Robotic exoskeletons have demonstrated their effectiveness in post-stroke gait rehabilitation therapy. Nevertheless, further research is being conducted to improve existing rehabilitation exoskeletons in terms of ease-of-use and innovative design. Previously, the adaptation of linkage-based mechanisms for rehabilitation exoskeletons has been considered an option. However, finding linkage parameters that will produce the required gait trajectories using a linkage-based exoskeleton, is quite challenging. It is furthermore challenging to obtain parameters of a linkage-based mechanism designed for a gait rehabilitation task that has to produce two trajectories (for knee and ankle joints) simultaneously. In this work, we propose Deep Generative Neural Networks (DGNN) to obtain a set of optimal dimensions and parameters for the Stephenson III six-bar linkage-based gait exoskeleton. The proposed methodology demonstrates high efficacy in determining the linkage parameters for various target trajectories. The proposed framework, once trained, can accurately predict mechanism parameters to achieve two joint trajectories simultaneously. Subsequent to developing the model, walking trajectories from healthy human subjects are given to the model to determine the optimal linkage dimensions of the gait rehabilitation exoskeleton. The proposed model can be used to assist designers in quickly determining the optimized linkage dimensions of linkage-based mechanisms that can provide various target trajectories.