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相关概念视频

Muscle Coordination and Action01:24

Muscle Coordination and Action

Muscle coordination is a complex and finely tuned process essential for smooth and purposeful movements like flexion, extension, adduction, abduction, and rotation. The human body orchestrates the actions of various muscles working in concert, each with a specific role. Four functional types describe how muscles work together: agonist, antagonist, synergist, and fixator.
Agonists
Agonist muscles, often called prime movers, are the primary muscles responsible for producing a specific movement.
Muscles that Move the Arm01:31

Muscles that Move the Arm

Nine muscles are involved in arm movements. Two of these, the pectoralis major and latissimus dorsi, originate from the axial skeleton and are called axial muscles. The other seven originate from the scapula and are called the scapular muscles.
The pectoralis major has two origins. Its clavicular head originates on the medial half of the clavicle. In contrast, the sternocostal head originates on the costal cartilages of ribs 1-6, the sternum, and the aponeurosis of the external oblique of the...

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相关实验视频

Updated: Jul 6, 2026

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
13:44

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多人形机器人手臂运动模拟和协作基于改进的重定位.

Xisheng Jiang1,2,3, Baolei Wu1, Simin Li1

  • 1School of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Biomimetics (Basel, Switzerland)
|March 26, 2025
PubMed
概括

这项研究引入了改进的人机交互 (HRI) 的运动重定位算法,提高了机器人手臂配置相似性和终端效应器准确性. 一种新的多人姿势估计方法使实时多机器人协作成为可能.

关键词:
人与机器人的互动改进了重新定位的改进.运动模拟模拟模拟多人姿势估计多人姿势估计

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Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms
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Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms

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Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes
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相关实验视频

Last Updated: Jul 6, 2026

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人与机器人的交互
  • 计算机视觉 计算机视觉

背景情况:

  • 人机交互 (HRI) 依赖于运动模仿,以实现高效的协作.
  • 现有的运动重定位算法在平衡机器人手臂配置相似性和终端效应器跟踪精度方面存在局限性.
  • 人类和机器人物理结构的差异对准确的运动传输构成了挑战.

研究的目的:

  • 为人类机器人开发一个改进的运动重定位算法.
  • 为了实现与人类相似的机器人手臂配置和精确的终端效应器位置跟踪.
  • 为了实现实时多人运动捕捉,用于协作机器人任务.

主要方法:

  • 提出了一个改进的重定位算法,将联合空间和笛卡尔空间考虑结合起来.
  • 引入了一个多人姿势估计算法,使用单个RGB-D摄像头进行实时运动捕捉.
  • 集成的姿势估计与重定位算法用于多机器人协作.

主要成果:

  • 改进的重定位算法成功确保了一致的机器人臂配置和准确的终端效应器跟踪.
  • 多人姿势估计算法展示了卓越的实时性能.
  • 实验结果在多机器人协作场景中验证了算法的有效性.

结论:

  • 提出的方法有效地解决了传统运动重定向方法的局限性.
  • 集成系统通过实时运动模拟,实现高效准确的多机器人协作.
  • 这项工作在复杂的HRI任务中提升了人形机器人的能力.