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

Journal: Scientific reports
PMID: 40263478

Abstract

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.

Authors

  • Ziang Liu
    Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
  • Xiangzhou Jian
    Department of Mechanical Engineering, Columbia University, New York, 10027, USA.
  • Touseef Sadiq
    Centre for Artificial Intelligence Research, Department of Information and Communication Technology, University of Agder, Jon Lilletuns vei 9, 4879 Grimstad, Norway.
  • Zaffar Ahmed Shaikh
    Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, 75660, Pakistan. zashaikh@bbsul.edu.pk.
  • Osama Alfarraj
    Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia.
  • Fahad Alblehai
    Computer Science Department, Community College, King Saud University, 11437, Riyadh, Saudi Arabia.
  • Amr Tolba
    Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia.

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