Towards Automated, Individualized, and Adaptive Lower-Limb Motor Training with a Force-Controlled Robotic Walker*

Individualized and adaptive motor training is more effective than standard “one-fits-all” programs because it can account for individual needs, provide challenging tasks, and adapt to the user’s performance. Automating this process is especially important in the face of a rapidly aging society and a shortage of skilled healthcare workers. The main challenges are automatic assessment of user performance, adaptive task difficulty, and dynamic exercise scheduling to provide an individualized training plan. The contributions of this paper are: 1) Overview of current literature on methods for individualized and adaptive motor training; 2) User study to evaluate the technical ability of the robotic walker RoboTrainer to provide individualized training; 3) Presentation of the novel framework RoboTrainerAID, which uses machine learning techniques for automatic assessment and spatial control actions for adaptive task difficulty. Evaluation of this framework will be conducted in a future user study. The goal of this paper is to highlight research gaps and provide concepts for the technical feasibility of robot-assisted, individualized, and adaptive lower-limb motor training and rehabilitation.