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

Inertial Frames of Reference01:03

Inertial Frames of Reference

Newton’s first law is usually considered to be a statement about reference frames. It provides a method for identifying a special type of reference frame: the inertial reference frame. In principle, we can make the net force on a body zero. If its velocity relative to a given frame is constant, then that frame is said to be inertial. So, by definition, an inertial reference frame is a reference frame where Newton's first law holds valid. Newton's first law applies to objects with constant...
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...

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

Dual-Branch Deep Learning for Continuous Gait Cycle Estimation with wearable IMU Sensors and Anthropometric Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Machine Learning-Based Optimization of tFUS Transducer Positioning for Targeted Visual Cortex Neuromodulation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Autism spectrum disorder disrupts brain network connectivity maturation during childhood development.

Scientific reports·2025
Same author

Correction: Myocardial scar and left ventricular ejection fraction classification for electrocardiography image using multi-task deep learning.

Scientific reports·2025
Same author

Validity and Reliability of the 'Feelfit®' Accelerometer in Evaluating Physical Activity and Sedentary Time in Children: A Comparative Study with Two Different Accelerometers.

International journal of exercise science·2025
Same author

Modified stereotactic neurosurgery techniques for rodent surgery enhance survival and reduce surgery time in a severe traumatic brain injury model.

Scientific reports·2025

相关实验视频

Updated: Jul 9, 2026

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
11:06

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

10.8K

从惯性测量单元 (IMU) 与ResUNet之间的肢体步行轨迹同步.

Noppawat Tantisiriwat, Napatsawan Ngamdi, Amorchai Khawkhom

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了新的自编码器网络,用于在步行康复期间同步四肢运动. 该系统在预测肢体间轨迹方面表现出高准确度,改善了步行康复策略.

    更多相关视频

    Home-Based Monitor for Gait and Activity Analysis
    07:24

    Home-Based Monitor for Gait and Activity Analysis

    Published on: August 8, 2019

    7.2K
    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

    8.4K

    相关实验视频

    Last Updated: Jul 9, 2026

    A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
    11:06

    A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

    Published on: April 12, 2016

    10.8K
    Home-Based Monitor for Gait and Activity Analysis
    07:24

    Home-Based Monitor for Gait and Activity Analysis

    Published on: August 8, 2019

    7.2K
    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

    8.4K

    科学领域:

    • 生物医学工程 生物医学工程
    • 康复机器人 康复机器人
    • 机器学习用于医疗保健

    背景情况:

    • 对于患有神经元损伤的患者来说,步行康复至关重要,这会显著影响生活质量.
    • 目前的康复经常忽视四肢协调的作用,将上肢和步行训练分开.
    • 肢体间协调是人类步态生理学的基础.

    研究的目的:

    • 开发和评估基于自动编码器的网络 (ResNetAE,ResUNet) 进行肢体间步行轨迹同步.
    • 通过同步上肢和下肢运动,使步行康复系统能够积极控制.
    • 通过采用整体,肢体间的方法来提高步行康复的有效性.

    主要方法:

    • 收集了30名健康受试者在三个年龄组 (年轻人,中年人,老年人) 的步态数据.
    • 使用Xsens MVN惯性测量单元 (IMU) 系统提取和同步身体部分位置.
    • 使用带有残余块的自编码网络 (ResNetAE,ResUNet) 来使用手和脚坐标数据预测肢体间的轨迹.

    主要成果:

    • 在预测四肢间轨迹时,达到0.0472米的平均绝对误差 (MAE).
    • 证明了高相同步,相同步指数 (PSI) 为96.70%.
    • 在未见对象的准确性和同步性方面超过了LSTMAE基线.

    结论:

    • 拟议的ResNetAE和ResUNet模型显示出对肢体间步行轨迹同步的显著前景.
    • 该系统的性能足以推动肢体间相位同步研究.
    • 这些发现支持了肢体间意识系统在步行康复外骨架技术中的潜在整合.