P. V. Sabique

About

P. V. Sabique is a researcher at the forefront of haptic feedback and machine learning for robotic-assisted minimally invasive surgery (RAMIS). His work focuses on restoring the critical sense of touch—lost in current robotic systems—by using deep learning to estimate and reproduce force feedback from visual data. Sabique’s major contributions include developing a data-driven recurrent neural network approach that accurately reproduces variable visuo-haptic force feedback during surgical tool insertion, a method that has garnered 17 citations. He has also systematically investigated how dimensionality reduction affects force estimation using recurrent and convolutional networks, a study cited 16 times. Most recently, Sabique proposed a stereovision-based approach employing a modified Inception ResNet V2 network to retrieve variable force feedback, further advancing the field. With a cumulative impact of over 40 citations on these key works, Sabique is making significant strides toward improving surgeon experience and patient outcomes in RAMIS by bridging the gap between visual information and tactile sensation.

Research Focus

Key Achievements

Top Papers

1. 1
   A data driven recurrent neural network approach for reproduction of variable visuo-haptic force feedback in surgical tool insertion

   17 citations · 2023
2. 2
   Investigating the influence of dimensionality reduction on force estimation in robotic-assisted surgery using recurrent and convolutional networks

   16 citations · 2023
3. 3
   A Stereovision-based Approach for Retrieving Variable Force Feedback in Robotic-Assisted Surgery Using Modified Inception ResNet V2 Networks

   7 citations · 2024

Key Collaborators

Contact & Links