Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Muscle Coordination and Action01:24

Muscle Coordination and Action

1.3K
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....
1.3K
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

465
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
465
Excitation-Contraction Coupling in Skeletal Muscles01:20

Excitation-Contraction Coupling in Skeletal Muscles

7.9K
Excitation-contraction coupling is a series of events that occur between generating an action potential and initiating a muscle contraction. It occurs at the triad, a structure found in skeletal muscle fibers that comprise a T-tubule and terminal cisternae of the sarcoplasmic reticulum on each side. These triads are visible in longitudinally sectioned muscle fibers. They are typically located at the A-I junction — the junction between the A and I bands of the sarcomere.
When an action...
7.9K
Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

11.9K
When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
11.9K
Development of the Limb Synovial Joints01:07

Development of the Limb Synovial Joints

1.3K
Joints form during embryonic development in conjunction with the formation and growth of the associated bones. The embryonic tissue that gives rise to all bones, cartilage, and connective tissues of the body is called mesenchyme.
The mesenchymal stem cells differentiate into chondrocytes that form the hyaline cartilage, and later the cartilaginous model of the bone. This model further transforms into a bone. This process is known as endochondral ossification.
During development, the limbs...
1.3K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

More isn't always better: Too much exoskeleton torque can disrupt balance.

bioRxiv : the preprint server for biology·2026
Same author

Temperature and moisture management and mitigation techniques in prosthetic sockets and liners: A rapid review.

Prosthetics and orthotics international·2026
Same author

Deep domain adaptation eliminates costly data required for task-agnostic wearable robotic control.

Science robotics·2025
Same author

Dynamic Duo: Design and Validation of an Autonomous Frontal and Sagittal Actuating Hip Exoskeleton for Balance Modulation During Perturbed Locomotion.

IEEE robotics and automation letters·2025
Same author

A Deep Learning Framework for End-to-End Control of Powered Prostheses.

IEEE robotics and automation letters·2025
Same author

The case against machine vision for the control of wearable robotics: Challenges for commercial adoption.

Science robotics·2025

相关实验视频

Updated: Jun 7, 2025

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

9.4K

通过生物关节时刻估计进行任务无关的外骨控制

Dean D Molinaro1,2,3, Keaton L Scherpereel4,5,6, Ethan B Schonhaut4

  • 1George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA. molinarodean@gmail.com.

Nature
|November 13, 2024
PubMed
概括

这项研究引入了一种用于下肢外骨架的新任务无关控制器,使用深度神经网络来估计关节时刻. 这使得人类的各种活动能够得到协调的帮助,从而提高了外骨的生存能力.

更多相关视频

Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand
06:44

Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand

Published on: May 20, 2020

7.0K
Experimental Methods to Study Human Postural Control
08:12

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

9.4K

相关实验视频

Last Updated: Jun 7, 2025

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

9.4K
Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand
06:44

Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand

Published on: May 20, 2020

7.0K
Experimental Methods to Study Human Postural Control
08:12

Experimental Methods to Study Human Postural Control

Published on: September 11, 2019

9.4K

科学领域:

  • 机器人技术
  • 生物力学
  • 人工智能

背景情况:

  • 目前的下肢外骨控制器难以处理各种人体运动, 限制了它们的实际应用.
  • 现有的系统往往需要特定任务的调整,这阻碍了活动之间的无过渡.

研究的目的:

  • 开发和评估用于下肢外骨架的无任务控制器,以帮助用户进行广泛的活动.
  • 通过使用深度神经网络来估计生物关节时刻, 实现自主多关节辅助.

主要方法:

  • 一个深度神经网络被训练在实时估计关节和膝关节的时刻.
  • 控制器被集成到服装集成的外骨架中,并在28个不同的活动中进行了测试.
  • 通过将估计的时刻与地面真相进行比较和评估用户的能量来评估性能.

主要成果:

  • 控制器准确地估计了28项活动的部和膝盖时刻 (平均R2为0.83),从循环运动到非结构化任务.
  • 基于任务的控制器显著优于基于任务分类器的方法.
  • 在没有控制器重新校准的情况下,使用者的能量减少了5. 319. 7%.

结论:

  • 使用基于深度神经网络的时刻估计的任务无关控制器可以有效地协调下肢外骨架的辅助.
  • 这种方法显著提高了外骨在广泛的人类行为中的适应性和效率.
  • 开发的控制器是对辅助外骨的现实可行性的关键一步.