Scholarlink

<p>This study introduces an innovative method for gesture recognition in medical robotics, utilizing Capsule Neural Networks (CNNs) in conjunction with the Modified Spring Search Algorithm (MSSA). This approach achieves remarkable efficiency in gesture identification, facilitating precise control over medical robots. The proposed system undergoes thorough evaluation through both simulations and practical experiments, showing its capability to enhance patient outcomes in robotic surgical procedures. The primary contributions of this research include the creation of a unique CNN-MSSA architecture for gesture recognition, an extensive assessment of the system’s performance, and evidence of its potential to advance patient care. The findings indicate that the system attains an accuracy rate of 95% with a processing duration of 0.5&#xa0;s, surpassing existing methodologies. These results carry significant implications for the advancement of autonomous medical robots and the enhancement of patient care in robotic surgery, underscoring the technology’s potential to improve the precision and efficiency of medical interventions.</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

Efficient control of spider-like medical robots with capsule neural networks and modified spring search algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月22日

查看全文
  • Ziang Liu,
  • Xiangzhou Jian,
  • Touseef Sadiq,
  • Zaffar Ahmed Shaikh,
  • Osama Alfarraj,
  • Fahad Alblehai,
  • Amr Tolba

摘要

This study introduces an innovative method for gesture recognition in medical robotics, utilizing Capsule Neural Networks (CNNs) in conjunction with the Modified Spring Search Algorithm (MSSA). This approach achieves remarkable efficiency in gesture identification, facilitating precise control over medical robots. The proposed system undergoes thorough evaluation through both simulations and practical experiments, showing its capability to enhance patient outcomes in robotic surgical procedures. The primary contributions of this research include the creation of a unique CNN-MSSA architecture for gesture recognition, an extensive assessment of the system’s performance, and evidence of its potential to advance patient care. The findings indicate that the system attains an accuracy rate of 95% with a processing duration of 0.5 s, surpassing existing methodologies. These results carry significant implications for the advancement of autonomous medical robots and the enhancement of patient care in robotic surgery, underscoring the technology’s potential to improve the precision and efficiency of medical interventions.