TY - GEN
T1 - Envibroscope
T2 - 2024 IEEE International Conference on Robotics and Automation, ICRA 2024
AU - Alikhani, Alireza
AU - Inagaki, Satoshi
AU - Dehghani, Shervin
AU - Maier, Mathias
AU - Navab, Nassir
AU - Nasseri, M. Ali
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Several robotic systems have emerged in the recent past to enhance the precision of micro-surgeries such as retinal procedures. Significant advancements have recently been achieved to increase the precision of such systems beyond surgeon capabilities. However, little attention has been paid to the impact of non-predicted and sudden movements of the patient and the environment. Therefore, analyzing environmental motion and vibrations is crucial to ensuring the optimal performance and reliability of medical systems that require micron-level precision, especially in real-life scenarios.To address this challenge, this paper introduces a novel environmental motion analysis system that employs a grid layout with distributed sensing nodes throughout the environment. This system effectively tracks undesired movements (motions) at designated locations and predicts upcoming motions using neural network-based approaches. The outcomes of our experiments exhibit promising prospects for real-time motion monitoring and prediction, which has the potential to form a solid basis for enhancing the automation, safety, integration, and overall efficiency of robot-assisted micro-surgeries.
AB - Several robotic systems have emerged in the recent past to enhance the precision of micro-surgeries such as retinal procedures. Significant advancements have recently been achieved to increase the precision of such systems beyond surgeon capabilities. However, little attention has been paid to the impact of non-predicted and sudden movements of the patient and the environment. Therefore, analyzing environmental motion and vibrations is crucial to ensuring the optimal performance and reliability of medical systems that require micron-level precision, especially in real-life scenarios.To address this challenge, this paper introduces a novel environmental motion analysis system that employs a grid layout with distributed sensing nodes throughout the environment. This system effectively tracks undesired movements (motions) at designated locations and predicts upcoming motions using neural network-based approaches. The outcomes of our experiments exhibit promising prospects for real-time motion monitoring and prediction, which has the potential to form a solid basis for enhancing the automation, safety, integration, and overall efficiency of robot-assisted micro-surgeries.
KW - AI for Medical Robotics
KW - Medical Robots and Systems
KW - Robot Safety
KW - Sensor-Based Navigation
UR - http://www.scopus.com/inward/record.url?scp=85202439514&partnerID=8YFLogxK
U2 - 10.1109/ICRA57147.2024.10611207
DO - 10.1109/ICRA57147.2024.10611207
M3 - Conference contribution
AN - SCOPUS:85202439514
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 8202
EP - 8208
BT - 2024 IEEE International Conference on Robotics and Automation, ICRA 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 May 2024 through 17 May 2024
ER -