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相关概念视频

Knee Joint01:23

Knee Joint

3.1K
The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris...
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Functional Classification of Joints01:09

Functional Classification of Joints

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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...
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相关实验视频

Updated: Jan 16, 2026

Movement Retraining using Real-time Feedback of Performance
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通过动态上下文聚焦和门式线性单位改进膝关节关节角度预测.

Lyes Saad Saoud1, Humaid Ibrahim2, Ahmad Aljarah3

  • 1Khalifa University Center for Autonomous and Robotic Systems, Khalifa University, Abu Dhabi, P O Box 127788, United Arab Emirates.

Computers in biology and medicine
|September 26, 2025
PubMed
概括

FocalGatedNet是一种新的深度学习模型,可以准确地预测实时的膝关节角度,用于生物力学和康复. 它的性能优于现有模型,在步行轨迹预测准确性和效率方面提供了显著的改进.

关键词:
注意力机制注意力机制外骨架辅助的康复疗法步态分析 步态分析门式线性单位 (GLU) 是指门式线性单位.膝关节角度预测 膝关节角度预测时间序列预测时间序列预测

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Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
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In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
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相关实验视频

Last Updated: Jan 16, 2026

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Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
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In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
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科学领域:

  • 生物力学和康复工程 生物力学和康复工程
  • 用于时间序列预测的深度学习
  • 可穿戴传感器数据分析数据分析

背景情况:

  • 准确的实时膝关节角度预测对于有效的生物力学分析和康复至关重要.
  • 现有的深度学习模型经常在步态数据中的复杂时间依赖性方面扎.
  • 对于实时应用,如外骨控制,需要强大高效的模型.

研究的目的:

  • 介绍FocalGatedNet,这是一个新的深度学习框架,用于多步行路径预测.
  • 使用动态上下文焦点 (DCF) 注意力和门式线性单位 (GLU) 增强功能依赖性捕获.
  • 提高膝关节角度预测的准确性和效率,用于实时生物机械应用.

主要方法:

  • 开发了FocalGatedNet,集成DCF注意力和GLU,用于高级时间依赖模型.
  • 在各种预测间隔 (20-100 ms) 的多式行走数据集上评估模型.
  • 进行了废除研究,以验证GLU和DCF注意力组件的贡献,并评估了传感器噪声的影响.

主要成果:

  • FocalGatedNet在预测准确度方面取得了实质性进展,表现优于基于变压器的模型.
  • 在平均绝对误差 (MAE),根平均平方误差 (RMSE) 和平均绝对百分比误差 (MAPE) 中实现了显著的减少.
  • 在移动条件下展示了增强的稳定性和高效的推理速度,并验证了现实世界的适用性.

结论:

  • FocalGatedNet为实时膝关节角度预测提供了一个高度准确和高效的解决方案.
  • 该模型的架构有效地捕捉了复杂的步态模式,证明对康复和外骨控制有好处.
  • FocalGatedNet代表了实时生物机械应用的可靠进步,代码可在GitHub上找到.