Smart Robotic Surgical Assistant Using Voice Command and Image Processing

This paper presents the development of a smart robotic surgical assistant that utilizes voice command and image processing to aid in surgical instrument handling. The system integrates a Dobot robotic arm controlled through verbal instructions to retrieve and position surgical tools, while an image recognition model, based on VGG16, identifies instruments in real-time from camera feeds. This automation enables hands-free operation, supporting sterile conditions and enhancing efficiency in the operating room (OR). A dataset comprising high-resolution surgical tool images was curated to fine-tune the VGG16 model, achieving over 95% accuracy in classification. Voice recognition, incorporated with OpenCV, reached a 92.5% accuracy rate in interpreting commands. This system addresses challenges in surgical tool management, offering an efficient and reliable alternative that reduces human error and improves workflow, signifying a major step toward integrated AI-robotics applications in healthcare.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-05
DOI
https://doi.org/10.5281/zenodo.14287552
Primary Topic
Soft Robotics and Applications
Type
article
Field-Weighted Citation Impact
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article

Smart Robotic Surgical Assistant Using Voice Command and Image Processing

Zenodo (CERN European Organization for Nuclear Research)
Soft Robotics and Applications
article

Smart Robotic Surgical Assistant Using Voice Command and Image Processing

article en

Abstract

This paper presents the development of a smart robotic surgical assistant that utilizes voice command and image processing to aid in surgical instrument handling. The system integrates a Dobot robotic arm controlled through verbal instructions to retrieve and position surgical tools, while an image recognition model, based on VGG16, identifies instruments in real-time from camera feeds. This automation enables hands-free operation, supporting sterile conditions and enhancing efficiency in the operating room (OR). A dataset comprising high-resolution surgical tool images was curated to fine-tune the VGG16 model, achieving over 95% accuracy in classification. Voice recognition, incorporated with OpenCV, reached a 92.5% accuracy rate in interpreting commands. This system addresses challenges in surgical tool management, offering an efficient and reliable alternative that reduces human error and improves workflow, signifying a major step toward integrated AI-robotics applications in healthcare.

Zenodo (CERN European Organization for Nuclear Research)
M S Ramaiah University of Applied Sciences (IN), Robotics Research (United States) (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 65%
Soft Robotics and Applications
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Smart Robotic Surgical Assistant Using Voice Command and Image Processing · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS