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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

54
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
54

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

Behavioral characterization of olfactory, auditory, and motor deficits in 5×FAD mice.

Journal of veterinary science·2026
Same author

Spicy food intake and dietary factors shape the gut microbiome and metabolism of mucin and short-chain fatty acids in healthy adults.

Scientific reports·2026
Same author

Evaluation of cavitation enhancements in low-boiling point (< -2°C) perfluorocarbon nanodroplet and microbubble mixtures using therapeutic ultrasound pulses.

Ultrasonics sonochemistry·2026
Same author

Streamlined Facial Data Collection Based on Utterance and Emotional Data for Human-to-Avatar Reconstruction.

IEEE transactions on visualization and computer graphics·2026
Same author

Discovery of key surface electromyography features during walking for discerning high and low muscle mass using machine learning analysis.

Scientific reports·2026
Same author

Diverse cultivation strategies are necessary to capture microbial diversity in High Arctic lake sediment.

Frontiers in microbiomes·2026

相关实验视频

Updated: Jul 1, 2025

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
13:35

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

10.5K

基于sEMG的萨尔科佩尼亚风险分类,使用经验模式分解和机器学习算法.

Konki Sravan Kumar1, Daehyun Lee1,2, Ankhzaya Jamsrandoj3

  • 1Center for Artificial Intelligence, Korea Institute of Science and Technology, Seoul, Korea.

Mathematical biosciences and engineering : MBE
|March 8, 2024
PubMed
概括

这项研究引入了一种使用表面电肌图 (sEMG) 和机器学习的新方法,以早期检测肉症风险. 该技术准确地识别身体活动期间肌肉健康风险,帮助预防策略.

关键词:
经验模式分解分解功能选择 功能选择机器学习是机器学习.sEMG 的意思是说.肉症的风险 肉症的风险

更多相关视频

The Creation of a Rat Model for Osteosarcopenia via Ovariectomy
04:23

The Creation of a Rat Model for Osteosarcopenia via Ovariectomy

Published on: February 21, 2025

309
Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography
07:33

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Published on: November 8, 2024

413

相关实验视频

Last Updated: Jul 1, 2025

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
13:35

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

10.5K
The Creation of a Rat Model for Osteosarcopenia via Ovariectomy
04:23

The Creation of a Rat Model for Osteosarcopenia via Ovariectomy

Published on: February 21, 2025

309
Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography
07:33

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Published on: November 8, 2024

413

科学领域:

  • 生物医学工程 生物医学工程
  • 运动学 运动学
  • 老年学是一门学科.

背景情况:

  • 在较年轻的年龄中检测萨科佩尼亚风险对于预防策略和健康的衰老至关重要.
  • 当前的方法在动态活动期间的早期识别中可能缺乏精度.

研究的目的:

  • 开发和验证一种用于早期发现肉症风险的新技术.
  • 将表面电肌图 (sEMG) 信号与实证模式分解 (EMD) 和机器学习 (ML) 结合起来.

主要方法:

  • 在健康和处于危险状态的个体的行走和坐活动中收集的sEMG数据.
  • 使用EMD从正常化的sEMG信号中提取内在模式函数 (IMF).
  • 对于特征选择的最小冗余性最大相关性 (mRMR),然后是ML分类与离开一个主体的交叉验证.

主要成果:

  • 获得的高准确率:0.88 (正常行走),0.89 (快速行走),0.81 (标准坐),0.80 (宽坐).
  • 通过sEMG-EMD-ML系统,可靠地确定了肉症风险.
  • 在各种体力任务中有效地分类患有肉症风险的个体.

结论:

  • 拟议的sEMG-EMD-ML系统提供了一种可靠和准确的方法,用于早期发现肉症风险.
  • 这项技术在肌肉功能评估和健康监测方面具有实际应用.
  • 早期识别支持改善肌肉质量和终身福祉的干预措施.