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

751
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...
751

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

Three-Dimensional Craniofacial Characteristics in Transfusion-Dependent and Non-Transfusion-Dependent Thalassemia Patients.

Journal of pediatric hematology/oncology·2026
Same author

Investigation of the interaction between shoulder and cervical vertebral pathologies in isolated shoulder pain: clinical and morphological aspects.

Journal of orthopaedic surgery and research·2026
Same author

Goniometric and 3D Motion Analysis in Shoulder ROM Assessment: A Reliability and Agreement Study Comparing Patients With Restricted Shoulder Range of Motion to Healthy Subjects.

Journal of chiropractic medicine·2026
Same author

Reliability assessment of markerless technologies in biomechanical motion analysis: a performance comparison.

Frontiers in sports and active living·2026
Same author

Evaluating facial paralysis: A comparative study of 2D Emotrics and 3D scanning technologies.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS·2025
Same author

Comparison of 3D surface and landmark-based analysis methods: The reliability and efficiency in determining asymmetry after facial palsy.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS·2025

相关实验视频

Updated: Apr 30, 2026

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

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.2K

面部运动分析中的深度传感器技术:与基于标记器的运动分析进行比较评估.

Beste Yilmaz1, Umut Ozsoy1, Yilmaz Yildirim1

  • 1Akdeniz University, Faculty of Medicine, Department of Anatomy, Antalya, Türkiye.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
|April 3, 2025
PubMed
概括

深度传感器提供可靠的面部运动跟踪,但与基于标记器的系统相比,显示微妙表情的偏差. 对于这种非侵入性技术的临床使用,需要进行算法改进.

关键词:
深度传感器 深度传感器面部运动分析 面部运动分析在Kinect V2中使用.基于标记的运动分析.可靠性 可靠性可靠性

更多相关视频

Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact
10:07

Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact

Published on: February 10, 2015

19.1K
Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
07:51

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

16.7K

相关实验视频

Last Updated: Apr 30, 2026

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

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.2K
Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact
10:07

Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact

Published on: February 10, 2015

19.1K
Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
07:51

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

16.7K

科学领域:

  • 生物医学工程 生物医学工程
  • 人类运动科学科学 人类运动科学
  • 计算机视觉 计算机视觉

背景情况:

  • 面部运动评估在临床诊断中至关重要.
  • 基于标记的运动分析是黄金标准,但具有侵入性和成本.
  • 深度传感器提供了一个非侵入性的,可能更容易获得的替代方案.

研究的目的:

  • 将深度传感器技术 (Kinect-V2) 的可靠性和一致性与面部动力学基于标记器的运动分析进行比较.
  • 为了评估深度传感器在捕捉面部运动中的准确性.

主要方法:

  • 100名健康参与者进行了6种不同的面部动作.
  • 同时记录使用基于标记器的系统和Kinect-V2深度传感器.
  • 分析包括不对称性,方法内部可靠性 (ICC) 和协议 (Bland-Altman).

主要成果:

  • 深度传感器显示出强大的方法内部可靠性 (ICC 0.61-0.85).
  • 布兰德-阿尔特曼分析显示,与基于标记器的系统相比,深度传感器的偏见和更广泛的协议限制,特别是对于微妙的表达.
  • 在微笑,闭眼和眉的方法之间,不对称值有所不同.

结论:

  • 深度传感器在面部运动分析方面具有良好的方法内可靠性.
  • 目前的深度传感器技术显示,在微妙的面部表情方面,存在显著的偏见和限制.
  • 进一步的算法改进是必要的,以提高面部动力学深度传感器的临床适用性.