A Computational Internal Model to Quantify the Effect of Sensorimotor Augmentation on Motor Output

The aging process, as well as neurological disorders, causes a decline in sensorimotor functions, which can often bring degraded motor output. As a means of compensation for such sensorimotor deficiencies, sensorimotor augmentation has been actively investigated. Consequently, exoskeleton devices or functional electrical stimulation could augment the muscle activity, while textured surfaces or electrical nerve stimulations could augment the sensory feedback. However, it is not easy to precisely anticipate the effects of specific augmentation because sensory feedback and motor output interact with each other as a closed-loop operation via the central and peripheral nervous systems. A computational internal model can play a crucial role in anticipating such an effect of augmentation therapy on the motor outcome. Still, no existing internal sensorimotor loop model has been represented in a complete computational form facilitating the anticipation. This paper presents such a computational internal model, including numerical values representing the effect of sensorimotor augmentation. With the existing experimental results, the model performance was evaluated indirectly. The change of sensory gain affects motor output inversely, while the change of motor gain did not change or minimally affects the motor output.Clinical Relevance— The presented computational internal model will provide a simple and easy tool for clinicians to design therapeutic intervention using sensorimotor augmentation.