title: Modeling and identification of human neuromusculoskeletal network based on biomechanical property of muscle.
abstract: In this paper, we build a whole-body neuromusculoskeletal network model including somatic reflex, and identify its parameters through non-invasive measurements and statistical analysis. Such models are crucial for analyzing and estimating signals in the nervous system. Our neuromuscular model consists of two parts. The first part models the neuromuscular network that represents the relationships between the spinal nerve signals and muscle activities, which are then converted to muscle tensions using a physiological muscle dynamics model. The second part includes the feedback loops from muscle spindles and Golgi tendon organs to the spinal nerve that represent the somatic reflex using muscle length, velocity, and tension information. We demonstrate the consistency of the model by showing that a forward dynamics simulation of somatic reflex yields a motion similar to actual human response.
authors.affiliation: Department of Mechano-Informatics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Japan. murai@ynl.t.u-tokyo.ac.jp
authors.forename: Akihiko
authors.initials: A
authors.lastname: Murai
authors.forename: Katsu
authors.initials: K
authors.lastname: Yamane
authors.forename: Yoshihiko
authors.initials: Y
authors.lastname: Nakamura
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D003198:Computer Simulation; D006801:Humans; D008953:Models, Anatomic; D008959:Models, Neurological; D015233:Models, Statistical; D009068:Movement; D009141:Musculoskeletal System / Q000294:innervation*; D009415:Nerve Net; D009420:Nervous System; D016571:Neural Networks, Computer; D012018:Reflex; D012815:Signal Processing, Computer-Assisted; D013710:Tendons / Q000502:physiology