Robotic assistance for rehabilitation has benefited from the use of models for motor adaptation. The assist-as-needed paradigm for rehabilitation robotics was based on a single-state model of human adaptation to a neurological handicap. Recent studies have shown that human motor adaptation consists of two or more parallel adaptation processes. A two-state model of adaptation based on the presence of a fast process and a slow process has been widely adopted. The fast process adapts faster than the slow process but has a lower retention than the slow process. Designing training methods that can influence the individual adaptation processes could help make sure that patients retain what is desired (how to adapt to a neurological injury) and forget what is detrimental to rehabilitation (dynamics of the robotic assistance for example). The goal of this work is to design an optimal control paradigm for selectively influencing the slow and fast processes.
A feedforward discrete-time linear-quadratic tracking controller was designed for a 2-state linear time-invariant model of sensorimotor adaptation to increase the contribution of the slow process to the net adaptation at the end of training. This control signal was implemented as the sequence of visuomotor rotations in an upper-limb reaching task. This sequence of visuomotor rotations were dubbed the Adaptation-State-Tracking (AST) perturbation. The retention behaviour after this AST perturbation was compared with that after a non-adaptive (constant-level) perturbation. A between-subject comparison of the retention behaviour showed that the AST perturbation exhibited better retention than the constant-level perturbation (p=0.0415). As far as the author is aware, this is first time the 2-state Linear Time-Invariant (LTI) model has been used to design a perturbation and to predict the subsequent behaviour of the participants. The sufficiency of the control based on the 2-state LTI model and the possibility of improving retention with optimal control could positively impact the domain of robot-assisted rehabilitation.