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脊髓反射對(duì)姿勢(shì)穩(wěn)定性的影響效果

發(fā)布時(shí)間:2018-03-14 01:37

  本文選題:神經(jīng)運(yùn)動(dòng)控制 切入點(diǎn):建模與仿真 出處:《上海交通大學(xué)》2012年碩士論文 論文類型:學(xué)位論文


【摘要】:姿勢(shì)的穩(wěn)定性在上肢的運(yùn)動(dòng)行為中是非常重要的,因?yàn)樯窠?jīng)運(yùn)動(dòng)系統(tǒng)時(shí)刻都面臨著內(nèi)部或者外部的不穩(wěn)定因素的干擾。剛度是一個(gè)常常用來刻畫上肢姿勢(shì)穩(wěn)定性的物理量,它由三個(gè)部分組成:內(nèi)部主動(dòng)分量、被動(dòng)分量、和反射引起的剛度。反射對(duì)剛度有重要的貢獻(xiàn),前面的實(shí)驗(yàn)已經(jīng)證明,由反射活動(dòng)產(chǎn)生的剛度可以根據(jù)運(yùn)動(dòng)任務(wù)的特點(diǎn)和環(huán)境做出相應(yīng)的改變,改變的最終目的都是通過增大剛度來減小不穩(wěn)定性。然而,受技術(shù)限制,實(shí)驗(yàn)方法至今不能對(duì)反射活動(dòng)對(duì)上肢端點(diǎn)剛度的影響方式這一問題做出深入的研究。本文中,我們?cè)谝粋(gè)已經(jīng)建好的開環(huán)的虛擬上臂模型的基礎(chǔ)上加入脊髓反射網(wǎng)絡(luò)模型,完成了一個(gè)閉環(huán)的虛擬上臂模型。用這兩個(gè)模型進(jìn)行的仿真實(shí)驗(yàn)可以幫助我們將反射活動(dòng)引起的剛度分離出來進(jìn)行研究。為了使仿真更接近真實(shí)情況,在模型的控制信號(hào)中加入了隨機(jī)噪聲。本研究中采用和實(shí)驗(yàn)方法相似的加入干擾力的方法來估計(jì)上肢端點(diǎn)剛度,上肢端點(diǎn)剛度用剛度橢圓表示。另外一個(gè)漂移橢圓用來表示上肢端點(diǎn)在神經(jīng)噪聲影響下的分布范圍。用開環(huán)和閉環(huán)模型分別做14組仿真實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果顯示,反射活動(dòng)可以使剛度的幅度平均增大18.37%,這個(gè)和實(shí)驗(yàn)方法的估計(jì)值一致。它還可以使漂移橢圓的幅度平均減小35%。同時(shí),剛度橢圓和漂移橢圓的形狀和方向不發(fā)生變化。這說明前向控制信號(hào)決定上臂的阻抗特性,反射只是起到增大阻抗的幅度的作用。另外,本文還對(duì)反射增益值對(duì)上臂的穩(wěn)定性的影響進(jìn)行了研究,結(jié)論是牽張反射的增益最容易引起不穩(wěn)定,腱反射的增益最不容易引起不穩(wěn)定。
[Abstract]:The stability of posture is very important in the movement behavior of upper limb, because the neural motor system always faces the disturbance of internal or external unstable factors. Stiffness is a physical quantity which is often used to describe the stability of upper limb posture. It consists of three parts: the internal active component, the passive component, and the stiffness caused by reflection, which makes an important contribution to stiffness, as has been demonstrated by previous experiments. The stiffness generated by the reflection activity can be changed accordingly according to the characteristics of the motion task and the environment. The ultimate purpose of the change is to reduce the instability by increasing the stiffness. In this paper, we add a spinal cord reflex network model based on an open loop virtual upper arm model. A closed loop virtual upper arm model is completed. The simulation experiments with these two models can help us to separate the stiffness caused by the reflection activities. In order to make the simulation more close to the real situation, Random noise is added to the control signal of the model. In this study, the stiffness of upper extremities is estimated by adding interference force similar to the experimental method. The stiffness of upper extremities is expressed by stiffness ellipse. Another drift ellipse is used to describe the distribution range of upper extremities under the influence of neural noise. Fourteen groups of simulation experiments are done with open loop and closed loop models respectively. The experimental results show that, The reflection activity can increase the stiffness amplitude by 18.37 on average, which is consistent with the experimental method. It can also reduce the average amplitude of the drift ellipse by 35 percent. The shape and direction of the stiffness ellipse and the drift ellipse do not change. This indicates that the forward control signal determines the impedance characteristics of the upper arm, and the reflection only increases the amplitude of the impedance. The effect of reflection gain on the stability of upper arm is also studied. It is concluded that the gain of stretch reflex is the most likely to cause instability, and the gain of tendon reflex is the least to cause instability.
【學(xué)位授予單位】:上海交通大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2012
【分類號(hào)】:R318.0

【共引文獻(xiàn)】

相關(guān)期刊論文 前1條

1 付春江;王如彬;;手臂屈伸運(yùn)動(dòng)中上位最優(yōu)控制對(duì)外部速度力場(chǎng)的補(bǔ)償適應(yīng)[J];中國(guó)生物醫(yī)學(xué)工程學(xué)報(bào);2010年04期

相關(guān)博士學(xué)位論文 前2條

1 鐘運(yùn)健;快速跑時(shí)下肢肌肉工作特性及乆繩肌拉傷風(fēng)險(xiǎn)的生物力學(xué)研究[D];上海體育學(xué)院;2010年

2 劉珊;人體上肢與上肢康復(fù)機(jī)器人運(yùn)動(dòng)控制研究[D];華中科技大學(xué);2008年

相關(guān)碩士學(xué)位論文 前1條

1 王穎;基于中樞模式發(fā)生器的下肢功能性電刺激康復(fù)系統(tǒng)設(shè)計(jì)與應(yīng)用[D];上海交通大學(xué);2012年

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