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

Measuring Acceleration Due to Gravity01:12

Measuring Acceleration Due to Gravity

693
Consider a coffee mug hanging on a hook in a pantry. If the mug gets knocked, it oscillates back and forth like a pendulum until the oscillations die out.
A simple pendulum can be described as a point mass and a string. Meanwhile, a physical pendulum is any object whose oscillations are similar to a simple pendulum, but cannot be modeled as a point mass on a string because its mass is distributed over a larger area. The behavior of a physical pendulum can be modeled using the principles of...
693
Three-Dimensional Force System01:30

Three-Dimensional Force System

2.3K
In mechanical engineering, a three-dimensional force system is a system of forces acting in three dimensions, with forces applied along the x, y, and z coordinate axes. The three-dimensional force system is an important concept in mechanical engineering, as it allows engineers to understand and analyze the behavior of objects and structures in three dimensions. By understanding the forces acting on a system, engineers can design more efficient and effective mechanical systems that can withstand...
2.3K
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

864
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
864
Two-Dimensional Force System01:20

Two-Dimensional Force System

1.0K
A two-dimensional system in mechanical engineering involves the analysis of motion and forces in a plane. A two-dimensional force vector can be resolved into its components as:
1.0K

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

Quantitative biomechanical analysis of sharp force injuries to the head using finite element simulation.

Forensic science, medicine, and pathology·2026
Same author

Biomechanical Investigation of Head Injuries Caused by Baseball Bat Strikes with Different Bat Sizes and Velocities: A Finite Element Simulation Study.

Life (Basel, Switzerland)·2026
Same author

Mechanics and muscular activity characteristics of the pinch grip across varying hold widths and climber skill levels.

Journal of sports sciences·2025
Same author

Spatial relationships among offender, knife, and victim during slashing attacks: implications for crime scene reconstruction.

International journal of legal medicine·2024
Same author

Validity of Actigraph for Measuring Energy Expenditure in Healthy Adults: A Systematic Review and Meta-Analysis.

Sensors (Basel, Switzerland)·2023
Same author

Effects of Footwear Selection on Plantar Pressure and Neuromuscular Characteristics during Jump Rope Training.

International journal of environmental research and public health·2023

相关实验视频

Updated: Sep 16, 2025

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

8.1K

使用基于IMU传感器的神经网络模型预测运行的垂直地面反应力.

Shangxiao Li1, Jiahui Pan1, Dongmei Wang2

  • 1Research Center for Sports Psychology and Biomechanics, China Institute of Sport Science, Beijing 100061, China.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
概括

这项研究开发了一种同步方法和人工神经网络 (ANN) 模型,用于使用惯性测量单元 (IMU) 数据在运行时预测垂直地面反应力 (vGRF). 这些模型准确地预测了vGRF,为个性化的下肢负荷监测提供了潜力.

关键词:
人工神经网络的人工神经网络与跑步有关的受伤.同步算法的同步算法可穿戴式传感器传感器

更多相关视频

Kinematics and Ground Reaction Force Determination: A Demonstration Quantifying Locomotor Abilities of Young Adult, Middle-aged, and Geriatric Rats
10:28

Kinematics and Ground Reaction Force Determination: A Demonstration Quantifying Locomotor Abilities of Young Adult, Middle-aged, and Geriatric Rats

Published on: February 22, 2011

19.8K
Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.3K

相关实验视频

Last Updated: Sep 16, 2025

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

8.1K
Kinematics and Ground Reaction Force Determination: A Demonstration Quantifying Locomotor Abilities of Young Adult, Middle-aged, and Geriatric Rats
10:28

Kinematics and Ground Reaction Force Determination: A Demonstration Quantifying Locomotor Abilities of Young Adult, Middle-aged, and Geriatric Rats

Published on: February 22, 2011

19.8K
Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.3K

科学领域:

  • 跑步的生物力学
  • 运动伤害预防 运动伤害预防
  • 可穿戴式传感器技术的技术.

背景情况:

  • 垂直地面反应力 (vGRF) 对于理解与跑步相关的伤害 (RRI) 至关重要.
  • 准确的vGRF测量通常需要仪器化跑步机或强力板.
  • 惯性测量单元 (IMU) 提供了一个便携式替代方案,但需要与vGRF数据同步.

研究的目的:

  • 在运行过程中开发和验证IMU和vGRF数据的同步方法 (STWS).
  • 创建和评估人工神经网络 (ANN) 模型 (WNN和FFNN) 来从IMU加速数据中预测vGRF.
  • 评估预测峰值vGRF和整体vGRF曲线的准确性.

主要方法:

  • 十五名跑步者 (后脚和前脚攻击者) 以每小时12,14和16公里的速度跑.
  • 一个单一的IMU和一个仪器化跑步机收集了加速和vGRF数据.
  • 滑动时间窗同步 (STWS) 算法用于调整IMU和vGRF数据. 波形神经网络 (WNN) 和前神经网络 (FFNN) 模型使用立场阶段的加速数据预测了vGRF.

主要成果:

  • 该STWS算法实现了步伐时间同步,平均绝对误差<11.2 ms.
  • ANN模型显示测量和预测的vGRF曲线之间存在很高的相关性 (r > 0.97) 和一致性 (NRMSE 4.69.2%,R2 0.930.99).
  • 峰值vGRF预测的NRMSEs为1.68.2%,斜轴加速数据显示出良好的准确性和减少的输入.

结论:

  • 在运行过程中,STWS算法有效地同步IMU和力板数据.
  • 无论是WNN和FFNN模型都准确地预测了vGRF,并且有可能仅仅使用 sagittal轴加速.
  • 这项研究为开发个性化ANN模型的基础,用于监测跑步者的下肢负荷.