Scholarlink

<p>The effectiveness of Convolutional Neural Networks (CNNs) in classifying hand gestures from surface electromyography (sEMG) signals is significantly influenced by the meticulous selection of hyperparameters. However, the impact of these structural hyperparameters on classification accuracy remains underexplored. This study focuses on systematically evaluating five structural CNN hyperparameters that have been minimally explored in previous research, through the implementation of a D-optimal experimental design. This methodology enables the systematic identification of efficient combinations while minimizing the number of experiments necessary for evaluation. Additionally, it allows for a thorough assessment of the individual contributions of each factor to accuracy and inference time, thereby highlighting the most influential parameters. Experimental trials were conducted using three distinct subsets from a public sEMG signal database (NinaPro). The findings indicate that the convolution type exerts the most significant influence on accuracy, closely followed by the number of parallel layers and, to a lesser extent, the number of sequential layers. Notably, one of the identified configurations attained an accuracy of 98.62% ± 0.84 on data obtained from subjects with transradial amputations at distinct levels. Furthermore, the most optimized models demonstrated inference times of below 200 ms on a standard processor, demonstrating their potential applicability in real-time environments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimizing CNN Hyperparameters for Enhanced sEMG Signal Classification using D-Optimal Design

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月21日

查看全文
  • Arturo A. Marquez Carranza,
  • Moises Arredondo-Velazquez,
  • Benito de Celis Alonso,
  • Eduardo Moreno-Barbosa

摘要

The effectiveness of Convolutional Neural Networks (CNNs) in classifying hand gestures from surface electromyography (sEMG) signals is significantly influenced by the meticulous selection of hyperparameters. However, the impact of these structural hyperparameters on classification accuracy remains underexplored. This study focuses on systematically evaluating five structural CNN hyperparameters that have been minimally explored in previous research, through the implementation of a D-optimal experimental design. This methodology enables the systematic identification of efficient combinations while minimizing the number of experiments necessary for evaluation. Additionally, it allows for a thorough assessment of the individual contributions of each factor to accuracy and inference time, thereby highlighting the most influential parameters. Experimental trials were conducted using three distinct subsets from a public sEMG signal database (NinaPro). The findings indicate that the convolution type exerts the most significant influence on accuracy, closely followed by the number of parallel layers and, to a lesser extent, the number of sequential layers. Notably, one of the identified configurations attained an accuracy of 98.62% ± 0.84 on data obtained from subjects with transradial amputations at distinct levels. Furthermore, the most optimized models demonstrated inference times of below 200 ms on a standard processor, demonstrating their potential applicability in real-time environments.