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. 2025 Apr 22;15(1):13828.
doi: 10.1038/s41598-025-95288-0.

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

Ziang Liu  1 , Xiangzhou Jian  2 , Touseef Sadiq  3 , Zaffar Ahmed Shaikh  4   5 , Osama Alfarraj  6 , Fahad Alblehai  6 , Amr Tolba  6
Affiliations

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

Ziang Liu et al. Sci Rep. .

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.

Keywords: Bio-inspired; Capsule neural network; Gesture-based control; MATLAB; Medical applications; Metaheuristic; Modified spring search algorithm; Robots.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
System arrangement of the proposed approach.
Fig. 2
Fig. 2
The simulation’s robots and obstacles.
Fig. 3
Fig. 3
The flowchart of the process.
Fig. 4
Fig. 4
Some instances of the examined database, which are utilized for training and validating the networks.
Fig. 5
Fig. 5
The model loss and accuracy profile.
Fig. 6
Fig. 6
Comparative results.
Fig. 7
Fig. 7
Variability in lighting, hand positioning, and surgical tool interactions.
Fig. 8
Fig. 8
Competitive computational complexity analysis: (A) Training time (hours), (B) Inference time per gesture (Seconds), and (C).
See this image and copyright information in PMC

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