【4/18更新】CiNii ArticlesのCiNii Researchへの統合について

立位時における人体前額面の姿勢制御モデルの検討(周波数応答実験による伝達関数モデルの同定)

  • 園部 元康
    徳島大学大学院ソシオテクノサイエンス研究部
  • 日野 順市
    徳島大学大学院ソシオテクノサイエンス研究部

書誌事項

タイトル別名
  • Study on modeling of human postural control on frontal-plane during standing (Identication of a transfer function model by frequency response test)

抄録

We have been developing a standing ride type vehicle, such as electric skateboards. A human dynamics model is required to assist balance control of human standing in the vehicle. So far, however, little information has emerged on the human balance control mechanism. The purpose of this study is to construct a transfer function model of human body dynamics on frontal plane when the support surface moves to horizontal direction. The present investigation deals with the dynamics in low frequency range (below 4rad/s) with parallel stance. We assumed a human mechanical model as one degree-of-freedom system. In a previous paper we estimated characteristic roots of the human standing model from impulse response tests. Here we report a procedure to identify a transfer function model describing the system definitely from the result of impulse response tests and frequency response tests. Reference acceleration for the support surface in the frequency response test was composed of six trigonometric functions from 1.0 rad/s to 4.0 rad/s. Posture angle of the body and moment generated from support surface were measured by a motion capture system and a load measurement system respectively. The human model was identified by two steps in the present method. We estimated the characteristic roots from the result of impulse response test in first step. Then it allowed us to define denominator of the transfer function model as the estimated characteristic polynomial. In second step, we identified numerator of the transfer function model from the result of frequency response test by least square method. The present method enabled us to identify a low-dimensional transfer function model accurately from experimental data with large variability.

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