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相关概念视频

Bone Structure01:55

Bone Structure

Within the skeletal system, the structure of a bone, or osseous tissue, can be exemplified in a long bone, like the femur, where there are two types of osseous tissue: cortical and cancellous.
Bone Remodeling01:40

Bone Remodeling

Bone remodeling is a continuous and balanced process of bone resorption by osteoclasts and bone formation by osteoblasts. In adults, it helps maintain bone mass and calcium homeostasis. While mechanical stress can stimulate turnover as part of the normal maintenance and reparative process, several hormones also regulate bone remodeling.

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相关实验视频

Updated: Jun 29, 2026

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
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Published on: July 22, 2014

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外骨执行器性能评估的系统框架.

Christian Di Natali1, Stefano Toxiri1, Stefanos Ioakeimidis1

  • 1Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy.

Wearable technologies
|July 25, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种基于学习的方法,用于评估外骨执行系统. 这种新方法提高了可穿戴机器人 (如 XoTrunk 外骨架) 的性能和控制.

关键词:
执行器 执行器 执行器动力学 动力学 动力学外是外骨的组成部分.任务分析 任务分析测试台的测试板.扭矩控制器的扭矩控制器

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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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Training Persons with Spinal Cord Injury to Ambulate Using a Powered Exoskeleton
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Training Persons with Spinal Cord Injury to Ambulate Using a Powered Exoskeleton

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相关实验视频

Last Updated: Jun 29, 2026

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 生物力学 生物力学
  • 人与机器人的交互

背景情况:

  • 包括外骨架在内的可穿戴设备越来越多地用于提高移动性,康复和工业援助.
  • 优化执行器选择对于高效的外骨架设计至关重要,传统上依靠动力学和动态分析.
  • 现有的方法提供了近似的执行器尺寸,但缺乏分析性能,控制和成本影响的深度.

研究的目的:

  • 介绍一种基于学习的新评估方法,用于分析外骨执行系统.
  • 为评估执行器性能和控制算法提供系统框架.
  • 通过详细分析,提高可穿戴机器人的实际运营效率.

主要方法:

  • 使用现实世界的实验设置来收集动力学和动态数据.
  • 执行系统模拟专注于电机性能和控制策略的开发.
  • 模拟结果经过实验验证,随后进行了现实世界的场景测试.
  • 该框架复制了人机交互动力学和动态,用于全面分析.

主要成果:

  • 开发的基于学习的方法提供了对执行系统的详细分析.
  • 系统框架可以更好地理解执行器性能和控制策略.
  • 在背部支外骨架上进行的实践表明,在行走任务的性能上有了显著的改善.

结论:

  • 提出的基于学习的评估方法为外骨执行器分析提供了强大的框架.
  • 这种方法可以提高可穿戴机器人的操作效率和与任务相关的性能.
  • 该研究强调了详细分析对于优化外骨设计和控制的重要性.