Generation of human reaching movement using a recurrent neural network model

Naomichi Ogihara, N. Yamazaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Human can spontaneously generate reasonable motion even for natural unconcerned gesture or unexperienced motion, in which we can not assume prior formation nor learning of an optimal trajectory. In this study, by contrast to the conventional trajectory planning approach, we attempt to construct a neuro-control mechanism that can spontaneously generate reaching motion without forming trajectory. The musculo-skeletal model of a human upper extremity is constructed as three rigid links with eight principal muscles. In order to represent passive joint resistance, a non-linear visco-elastic element is attached around each joint. The nervous system is modeled as a recurrent neural network which incorporates the human musculo-skeletal potential and a pseudo-potential which defines a goal position. Given a goal position, the nervous system thus generates muscular activation signals that tend to move the hand to the goal while decreasing the musculo-skeletal potential. Due to the dynamic interaction among the entire neuro-musculo-skeletal systems, the model can generate reaching movement as if hand position is attracted to the reaching goal while being naturally affected by the inherent musculo-skeletal constraints of human upper limb. Comparisons of the generated motions with measured data demonstrate that the model is capable of inducing human-like reaching motion towards a given goal position without priorly computing an optimal trajectory. The simulated result suggests that the proposed neural network model may describe a spontaneous motion generating mechanism which human may posses inherently.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
PublisherIEEE
Volume2
Publication statusPublished - 1999
Event1999 IEEE International Conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics' - Tokyo, Jpn
Duration: 1999 Oct 121999 Oct 15

Other

Other1999 IEEE International Conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics'
CityTokyo, Jpn
Period99/10/1299/10/15

Fingerprint

Recurrent neural networks
Trajectories
Neurology
Musculoskeletal system
Muscle
Chemical activation
Neural networks
Planning

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering

Cite this

Ogihara, N., & Yamazaki, N. (1999). Generation of human reaching movement using a recurrent neural network model. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (Vol. 2). IEEE.

Generation of human reaching movement using a recurrent neural network model. / Ogihara, Naomichi; Yamazaki, N.

Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 2 IEEE, 1999.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ogihara, N & Yamazaki, N 1999, Generation of human reaching movement using a recurrent neural network model. in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. vol. 2, IEEE, 1999 IEEE International Conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics', Tokyo, Jpn, 99/10/12.
Ogihara N, Yamazaki N. Generation of human reaching movement using a recurrent neural network model. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 2. IEEE. 1999
Ogihara, Naomichi ; Yamazaki, N. / Generation of human reaching movement using a recurrent neural network model. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Vol. 2 IEEE, 1999.
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