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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
Machines: Problem Solving I01:22

Machines: Problem Solving I

A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
Machines: Problem Solving II01:30

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.

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Investigating Motor Skill Learning Processes with a Robotic Manipulandum
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个性化机器人训练在平面达标任务上

Bruno Borghi, Naveed Reza Aghamohammadi, Adriana Cancrini

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    概括
    此摘要是机器生成的。

    这项研究表明,定制的机器人力量通过调整内部大脑模型来改善运动学习. 这种神经适应控制增强了上肢运动,并有可能进行神经康复.

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    科学领域:

    • 神经科学是一个神经科学.
    • 机器人技术 机器人技术 机器人技术
    • 发动机控制器的控制器

    背景情况:

    • 最近神经适应控制方面的进展.
    • 了解机器人干扰的运动适应对于康复至关重要.

    研究的目的:

    • 评估一种用于定制训练部队的新代算法.
    • 研究机器人产生的干扰,在上肢达到任务的运动适应.
    • 假设进食转发命令和神经肌肉系统重塑的变化.

    主要方法:

    • 为定制训练部队实施了一种新的代算法.
    • 利用机器人产生的干扰来实现上肢的任务.
    • 对比了两个扰动条件:卷曲力场和错误场力.

    主要成果:

    • 机器人产生的力量通过内部模型适应导致轨迹的修改.
    • 观察到性能改善,通过降低位置误差来衡量.
    • 与卷曲力场相比,错误场力在增强运动学习方面表现出更大的有效性.

    结论:

    • 定制的机器人扰动力有效地提高了运动学习和性能.
    • 内部模型适应在对外部干扰的运动补偿中起着关键作用.
    • 这种方法显示了改善个人运动活动和推进神经康复技术的潜力.