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相关概念视频

Functional Classification of Joints01:09

Functional Classification of Joints

3.8K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
3.8K
Structural Classification of Joints01:20

Structural Classification of Joints

3.1K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.1K
Stereotype Content Model02:16

Stereotype Content Model

13.9K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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相关实验视频

Updated: May 28, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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探索人类姿势识别研究中的趋势和集群:使用CiteSpace的分析.

Lichuan Yan1, You Du1

  • 1College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China.

Sensors (Basel, Switzerland)
|February 13, 2025
PubMed
概括

这项研究绘制了人类姿势识别研究的地图,强调了深度学习和传感器方法. 该领域正在成熟,未来的创新可能在跨学科生物力学和工程集成方面.

科学领域:

  • 计算机科学 计算机科学
  • 生物医学工程 生物医学工程
  • 人与计算机的交互

背景情况:

  • 人体姿势识别对于医疗保健,体育和人机交互的应用至关重要.
  • 现有的研究涵盖了基于视觉和非基于视觉的方法.
  • 该领域已经看到由机器学习和传感器技术推动的重大进步.

研究的目的:

  • 系统地分析人类姿势识别的跨学科研究方向.
  • 确定关键趋势,知识结构和创新途径.
  • 为未来的研发提供路线图.

主要方法:

  • 使用CiteSpace软件对3066篇研究论文 (2011-2024) 进行了图书统计分析.
  • 深入引用分析以确定研究集群和关键文件.
  • 分析出版物增长趋势和跨学科指标 (例如,引用爆发,中心性).

主要成果:

  • 确定了五个重要的研究集群,其中深度学习和基于传感器的方法主导了最近的进展.
  • 深度学习模型以最小的延迟实现了高精度 (例如99.7%).
  • 该领域显示出成熟的迹象,主要突破预计将放缓.
  • 显而易见的趋势是跨学科方法,将生物力学与工程结合起来,正在出现.
关键词:
在CiteSpace中使用CiteSpace.人类活动识别 (HAR)人类姿势识别技术惯性测量单位 (IMU) 是指惯性测量单位.知识图表知识图表视觉分析 视觉分析

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相关实验视频

Last Updated: May 28, 2025

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结论:

  • 人体姿势识别是一个成熟的领域,目前由深度学习和传感器技术主导.
  • 未来的创新在于生物力学,工程和先进的计算方法的融合.
  • 该研究提供了一个全面的概述,以指导未来的研究和识别创新差距.