首页> 外文期刊>Journal of Neurophysiology >Control strategies correcting inaccurately programmed fingertip forces: model predictions derived from human behavior.
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Control strategies correcting inaccurately programmed fingertip forces: model predictions derived from human behavior.

机译:纠正不正确编程的指尖力的控制策略:源自人类行为的模型预测。

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When picking up a familiar object between the index finger and the thumb, the motor commands are predetermined by the CNS to correspond to the frictional demand of the finger-object contact area. If the friction is less than expected, the object will start to slip out of the hand, giving rise to unexpected sensory information. Here we study the correction strategies of the motor system in response to an unexpected frictional demand. The motor commands to the mononeuron pool are estimated by a novel technique combining behavioral recordings and neuromuscular modelling. We first propose a mathematical model incorporating muscles, hand mechanics, and the action of lifting an object. A simple control system sends motor commands to and receives sensory signals from the model. We identify three factors influencing the efficiency of the correction: the time development of the motor command, the delay between the onset of the grip and load forces (GF-LF-delay), and how fast the lift is performed. A sensitivityanalysis describes how these factors affect the ability to prevent or stop slipping and suggests an efficient control strategy that prepares and corrects for an altered frictional condition. We then analyzed fingertip grip and load forces (GF and LF) and position data from 200 lifts made by five healthy subjects. The friction was occasionally reduced, forcing an increase of the GF to prevent the object being dropped. The data were then analyzed by feeding it through the inverted model. This provided an estimate of the motor commands to the motoneuron pool. As suggested by the sensitivity analysis the GF-LF-delay was indeed used by the subjects to prevent slip. In agreement with recordings from neurons in the primary motor cortex of the monkey, a sharp burst in the estimated GF motor command (NGF) efficiently arrested any slip. The estimated motor commands indicate a control system that uses a small set of corrective commands, which together with the GF-LF-delay form efficient correction strategies. The selection of a strategy depends on the amount of tactile information reporting unexpected friction and how long it takes to arrive. We believe that this technique of estimating the motor commands behind the fingertip forces during a precision grip lift can provide a powerful tool for the investigation of the central control of the motor system.
机译:当在食指和拇指之间捡起熟悉的物体时,CNS会预先确定电动机命令,以对应于手指与物体接触区域的摩擦要求。如果摩擦力小于预期,则物体将开始滑出手部,从而产生意想不到的感觉信息。在这里,我们研究了响应意外摩擦需求的电机系统的校正策略。通过结合行为记录和神经肌肉建模的新技术来估计单神经元池的运动命令。我们首先提出一个包含肌肉,手部力学以及举起物体的动作的数学模型。一个简单的控制系统向模型发送电动机命令并从模型接收感觉信号。我们确定了影响校正效率的三个因素:电机指令的时间发展,抓地力和负载力之间的延迟(GF-LF延迟)以及执行提升的速度。敏感性分析描述了这些因素如何影响预防或停止打滑的能力,并提出了一种有效的控制策略,该策略可为摩擦条件的变化进行准备和纠正。然后,我们分析了五名健康受试者进行的200次举重的指尖握力和负载力(GF和LF)和位置数据。偶尔会减小摩擦,从而迫使GF增加以防止物体掉落。然后通过将数据输入反演模型进行分析。这提供了对运动神经元池的电动机命令的估计。如敏感性分析所建议,受试者确实使用了GF-LF延迟来防止滑倒。与猴子初级运动皮层神经元的记录一致,估计的GF运动指令(NGF)突然爆发有效地阻止了任何滑倒。估计的电动机命令指示使用少量校正命令的控制系统,该校正命令与GF-LF延迟一起形成有效的校正策略。策略的选择取决于报告意外摩擦的触觉信息量以及到达所需时间。我们相信,这种在精确的握力提升过程中估算指尖力背后的电动机命令的技术可以为研究电动机系统的中央控制提供强大的工具。

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