Full-body Exoskeleton Robot Control for Walking Assistance by Style-phase Adaptive Pattern Generation

We propose an adaptive walking assistance strategy to control an exoskeleton robot. In our proposed framework, we explicitly consider the following: 1) the diversity of user motions (style) and 2) the interactions among a user, a robot, and an environment. To spatially coordinate a wide variety of user motions and robot behaviors, we estimated style parameters from observed user movements. To temporally coordinate the interactions among the user, the robot, and the environment, we synchronized the phases of these three systems with a coupled oscillator model. The estimated style parameters and the phase of the user motion can be used to predict future user movements. We investigated how movement prediction and phase synchronization can be beneficial to control an exoskeleton robot. To evaluate our adaptive walking assistance strategy, we developed simulated user and exoskeleton models. The physical interactions among the user, the exoskeleton, and the ground models are introduced in the simulated system. We show that the necessary torque for the user walking movement was reduced around 40% by using our proposed method to control the exoskeleton model.