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相关概念视频

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Knee Joint01:23

Knee Joint

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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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相关实验视频

Updated: Jul 23, 2025

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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从表面EMG估计膝关节角度使用多个内核相关性向量回归.

Hui-Bin Li1, Xiao-Rong Guan1, Zhong Li1

  • 1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括

这项研究引入了一种新的多核相关向量回归 (MKRVR) 方法,用于估计可穿戴机器人表面电肌图 (sEMG) 信号的膝关节角度,从而增强人机交互.

关键词:
关节角度估计 关节角度估计动议 意图 意图 动议表面电肌图 (sEMG) 是一种表面电肌图.

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相关实验视频

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 生物医学工程 生物医学工程
  • 机器学习 机器学习

背景情况:

  • 表面电肌图 (sEMG) 信号对于可穿戴机器人的运动意图识别至关重要.
  • 精确的膝关节角度估计对于有效的人机交互和降低模型复杂性至关重要.

研究的目的:

  • 提出并验证一种新的多核相关向量回归 (MKRVR) 模型,用于使用sEMG信号估计膝关节的角度.
  • 将MKRVR模型的性能与最小平方支向量回归 (LSSVR) 模型进行比较.

主要方法:

  • 离线学习使用多个内核相关性向量回归 (MKRVR).
  • 使用根平均平方误差 (RMSE),平均绝对误差 (MAE) 和R2_score进行性能评估.
  • 在MKRVR和最小平方支向量回归 (LSSVR) 模型之间进行比较分析.

主要成果:

  • 与LSSVR相比,MKRVR模型在估计膝关节角度方面表现优异.
  • 实现了连续的全球MAE为3.27° ± 1.2°,RMSE为4.81° ± 1.37°,R2为0.8946 ± 0.07.
  • 验证了MKRVR的可行性,用于从sEMG数据中准确地估计膝关节角度.

结论:

  • 该MKRVR方法是从sEMG信号估计膝关节角度的可行方法.
  • 这种技术可以应用于运动分析和识别穿着者在人机协作中的运动意图.
  • 提高可穿戴机器人应用中的控制系统的精度和可靠性.