A recurrent neural network model for generation of humanlike reaching movements

Yuta Tsuzuki, Naomichi Ogihara

Research output: Contribution to journalArticlepeer-review


We constructed a recurrent neural network model that can generate human reaching motion. Given a target position, the neural network model is capable of producing muscular activation signals that move the hand to the target endpoint, while solving the problem of musculoskeletal redundancy by dynamic relaxation of the energy functions embedded in the network. The proposed neural network model was integrated with a two-dimensional three-link eight muscle musculoskeletal model of the human arm to simulate arm reaching movements in the horizontal and sagittal planes. Our results demonstrate that the model is capable of generating natural arm movements that have spatiotemporal features, such as a slightly curved hand path and the characteristic bell-shaped velocity profile, that are similar to those of actual human movements. Some aspects of the proposed computational framework might be utilized in the central nervous system for generation of reaching movements.

Original languageEnglish
Pages (from-to)837-849
Number of pages13
JournalAdvanced Robotics
Issue number15
Publication statusPublished - 2018 Aug 3


  • Musculoskeletal system
  • dynamics
  • motor control
  • muscle synergy
  • redundancy

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications


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