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

Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

717
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
717

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

100 Normative Gait Profiles with 5-year fall tracking: Benchmark Dataset for Southeast Asian Movement Science.

Scientific data·2026
Same author

Prediction for prospective falls via gait evaluation using mobile devices for stroke survivors: A markerless motion analysis study.

Clinical rehabilitation·2026
Same author

Simulating Safe Bite Transfer in Robot-Assisted Feeding with a Soft Head and Articulated Jaw.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same author

Design and Evaluation of a Single-Sided Mobility Assistive Exoskeleton (SMAEXO) for Hemiplegia.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same author

Muscle Activation and Postural Sway in Response to Task Complexity: A Study of Balance Control in Older Adults.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same author

Design of a Breakaway Utensil Attachment for Enhanced Safety in Robot-Assisted Feeding.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025

相关实验视频

Updated: Apr 17, 2026

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.4K

解剖学标记驱动的3D无标记的人类运动捕捉.

Prayook Jatesiktat, Guan Ming Lim, Wee Sen Lim

    IEEE journal of biomedical and health informatics
    |July 9, 2024
    PubMed
    概括
    此摘要是机器生成的。

    使用深度学习的无标记运动捕捉 (mocap) 为传统基于标记器的系统提供了替代方案. 这项研究引入了一种用于精确2D关键点注释的新方法,提高了生物力学研究中的3D标记位置精度.

    更多相关视频

    Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
    09:32

    Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

    Published on: April 11, 2018

    9.7K
    3D Kinematic Gait Analysis for Preclinical Studies in Rodents
    10:19

    3D Kinematic Gait Analysis for Preclinical Studies in Rodents

    Published on: August 3, 2019

    10.7K

    相关实验视频

    Last Updated: Apr 17, 2026

    Movement Retraining using Real-time Feedback of Performance
    08:16

    Movement Retraining using Real-time Feedback of Performance

    Published on: January 17, 2013

    13.4K
    Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
    09:32

    Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

    Published on: April 11, 2018

    9.7K
    3D Kinematic Gait Analysis for Preclinical Studies in Rodents
    10:19

    3D Kinematic Gait Analysis for Preclinical Studies in Rodents

    Published on: August 3, 2019

    10.7K

    科学领域:

    • 生物力学 生物力学
    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 基于标记器的运动捕捉 (mocap) 精确但耗时.
    • 使用深度学习的无标记式mocap是有希望的,但对注释错误敏感.
    • 改进2D关键点注释对于无标记模拟图的准确性至关重要.

    研究的目的:

    • 开发一个精确的2D关键点注释方法,用于无标记的mocap.
    • 创建一个高质量的注释数据集 (RRIS40) 用于训练深度学习模型.
    • 为了验证拟议的无标记的mocap系统与基于标记的系统的准确性.

    主要方法:

    • 使用基于标记的mocap系统进行同步,校准的RGB摄像头设置.
    • 创建了RRIS40数据集,其中的表面解剖学地标是根据基于标记器的mocap数据进行注释的.
    • 训练了一个深度神经网络来估计二维解剖地标,并使用射线距离三角测量来确定三维位置.

    主要成果:

    • 在3D标记位置中获得了13.23mm的平均欧几里德误差,与标记位置精度相比较.
    • 与OpenCap对3D解剖学标志的增强相比,拟议的方法表现出更高的性能.
    • 包含10名受试者的数据的RRIS40测试套件是公开的.

    结论:

    • 这种新的方法通过改进2D关键点注释来提高无标记模拟图的精度.
    • 这种方法为生物力学中传统的基于标记的mocap提供了可行的和准确的替代方案.
    • 通过提高准确性和可访问性,促进在科学研究中更广泛地采用无标记MOCAP.