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相关概念视频

Bones of the Upper Limb: Radius01:09

Bones of the Upper Limb: Radius

The radius is longer of the two bones that make up the human antebrachium or forearm. At the proximal end, the radius articulates with the capitulum of the humerus and the radial notch of the ulna to form the elbow joint. At the distal end, the radius articulates with the ulna via the ulnar notch, forming the distal radioulnar joint. Distally, the radius also attaches to the carpal wrist bones (scaphoid and lunate) to form the radiocarpal joint.
The radius has a nail-shaped head, and a short...
Muscles of the Forearm that Move the Hand and Fingers01:16

Muscles of the Forearm that Move the Hand and Fingers

The muscles of the forearm that move the wrist, hand, and digits are numerous and diverse. They can be classified into two groups based on their location and function — the anterior and posterior compartment muscles.
Anterior Compartment
The anterior compartment muscles originate from the humerus. They primarily function as flexors and are also known as flexor muscles. They typically insert on the carpals, metacarpals, and phalanges. The superficial layer includes the flexor carpi radialis,...

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相关实验视频

Updated: Jun 30, 2026

Visualizing Motion Patterns in Acupuncture Manipulation
08:18

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MetaAcuPoint:MetaHuman生成的合成数据用于手部针点定位.

Kasunika Guruge1, Prathiksha Padmanabha1, H M K K M B Herath1

  • 1Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Republic of Korea.

Healthcare (Basel, Switzerland)
|December 11, 2025
PubMed
概括

本研究介绍了MetaAcuPoint,这是一个合成数据集,用于在传统韩国医学中精确的针点定位. 合成数据使得准确的,标准化的针点识别,克服传统方法的局限性.

关键词:
超人类 (MetaHuman) 是一个超人类.针点本地化定位标注一致性 标注一致性深度学习是一种深度学习.综合数据 综合数据虚拟现实 虚拟现实 虚拟现实 虚拟现实

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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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相关实验视频

Last Updated: Jun 30, 2026

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08:18

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Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation
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科学领域:

  • 生物医学工程 生物医学工程
  • 医疗成像医学成像
  • 韩国传统医学 韩国传统医学

背景情况:

  • 精确的针点 (针点) 定位对于韩国传统医学 (TKM) 临床成功至关重要.
  • 传统的针点识别方法是主观的,阻碍了标准化.
  • 现有的数据驱动技术面临挑战,原因是有限的,不一致的标签数据集.

研究的目的:

  • 介绍MetaAcuPoint,一种用于高保真性针点定位的新型合成数据集.
  • 为了在TKM研究中克服数据集限制,提供解剖学上一致的手部图像.
  • 为了促进标准化,数据驱动的针点识别.

主要方法:

  • 在虚幻引擎中使用MetaHuman化身生成了900张RGB手像.
  • 实施精确的,与骨头相连的插座,用于五个手部针点,精确到毫米.
  • 通过训练高分辨率网络 (HRNet-W48) 和对真实世界的图像进行测试来评估数据集的有效性.

主要成果:

  • 合成训练模型的平均距离误差 (MDE) 为5.67像素,与真实数据基线 (4.81像素) 相比.
  • 当合成数据被添加到真实数据 (MDE:4.95像素) 中时,合成数据提高了性能.
  • 合成数据训练模型比真实数据训练模型更好地对外部数据集进行概括 (MDE:5.84-6.45 mm vs 10.63-15.80 mm).

结论:

  • "MetaAcuPoint"代表了手部针点定位的第一个合成到现实通用化.
  • 数据集结合了摄影现实化的染与基于解剖学的注释.
  • MetaAcuPoint为推进标准化,数据驱动的针研究和实践提供了一个可靠的资源.