TY - GEN
T1 - A Computational Internal Model to Quantify the Effect of Sensorimotor Augmentation on Motor Output
AU - Dollahon, Devon
AU - Ryu, Seok Chang
AU - Park, Hangue
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85091019262&partnerID=8YFLogxK
U2 - 10.1109/EMBC44109.2020.9176109
DO - 10.1109/EMBC44109.2020.9176109
M3 - Conference contribution
AN - SCOPUS:85091019262
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3751
EP - 3754
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -