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

56
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...
56

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

Artificial Intelligence Algorithm Based on Genetics to Predict Responses to Interferon-Beta Treatment in Multiple Sclerosis Patients.

Bioengineering (Basel, Switzerland)·2026
Same author

Sensory-Cognitive Profiles in Children with ADHD: Exploring Perceptual-Motor, Auditory, and Oculomotor Function.

Bioengineering (Basel, Switzerland)·2025
Same author

The Integration of Artificial Intelligence with Micro-Nano-Systems: Perspectives, Challenges and Future Prospects.

Micromachines·2025
Same author

Model Parametrization-Based Genetic Algorithms Using Velocity Signal and Steady State of the Dynamic Response of a Motor.

Biomimetics (Basel, Switzerland)·2025
Same author

Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques.

Biomimetics (Basel, Switzerland)·2025
Same author

Perceptual-Motor Abilities and Reversal Frequency of Letters and Numbers in Children Diagnosed with Poor Reading Skills.

Bioengineering (Basel, Switzerland)·2025

相关实验视频

Updated: Jul 5, 2025

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

534

为EMG信号分类优化RNN:使用灰狼优化的新策略

Marcos Aviles1, José Manuel Alvarez-Alvarado1, Jose-Billerman Robles-Ocampo2,3

  • 1Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76010, Mexico.

Bioengineering (Basel, Switzerland)
|January 22, 2024
PubMed
概括

循环神经网络 (RNN) 在对上肢运动的电肌图 (EMG) 信号进行分类时达到100%的准确性,性能优于支向量机 (SVM) 并提供更快的分类速度.

关键词:
在EMGEMGEMGEMGEMGEMGEMGEMGEM在这里,GRU GRU GRU这就是GWO GWO.这是LSTM的LSTM.一个RNN RNN双向循环神经网络是双向循环神经网络.听算法 (Metaheuristic Algorithm) 是一种算法,可以通过

更多相关视频

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

551
A Real-Time Wearable Electromyography Measurement System for Small Animals
05:00

A Real-Time Wearable Electromyography Measurement System for Small Animals

Published on: November 15, 2024

636

相关实验视频

Last Updated: Jul 5, 2025

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

534
Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

551
A Real-Time Wearable Electromyography Measurement System for Small Animals
05:00

A Real-Time Wearable Electromyography Measurement System for Small Animals

Published on: November 15, 2024

636

科学领域:

  • 生物医学工程 生物医学工程
  • 机器学习 机器学习
  • 信号处理 信号处理

背景情况:

  • 精确的电肌图 (EMG) 信号分类对于开发先进的生物医学应用和假肢非常重要.
  • 循环神经网络 (RNN) 在处理序列数据方面表现有前途,使其适合分析动态EMG信号.

研究的目的:

  • 评估和比较不同RNN架构 (LSTM,GRU,双向RNN) 与支持矢量机器 (SVM) 的性能,用于对EMG信号进行分类.
  • 评估这些模型对五种不同的右上肢运动的准确性和分类速度.

主要方法:

  • 使用Butterworth波器预处理EMG信号,并将其细分为250毫秒的窗口,覆盖190毫秒.
  • 使用灰狼优化来调整GRU,LSTM和双向RNN架构的参数.
  • 通过将分类准确度和响应时间与SVM进行比较来评估性能.

主要成果:

  • 所有评估的RNN架构都实现了100%的分类准确性,超过了SVM在初始阶段93%的准确性.
  • 长短期记忆 (LSTM) 显示出0.12毫秒的最快分类速度,紧随其后的是GRU和双向RNN.
  • 在第二阶段,RNN保持高精度 (96.38%-98.46%),而SVM性能没有详细说明.

结论:

  • 循环神经网络,特别是LSTM,在EMG信号分析方面提供了更高的准确性和更快的分类速度,与SVM等传统方法相比.
  • 这些发现强调了RNN在诸如假肢控制和人机交互等领域的实时应用中的潜力.