Sara Freund

About

Sara Freund is a pioneering researcher at the intersection of medical robotics and advanced sensing technologies. Her work focuses on developing high-precision tracking and shape-sensing systems for continuum robots used in minimally invasive surgery. Freund’s major contributions include the proof of concept for a novel absolute rotary encoder, which addresses the critical challenge of achieving high resolution in small-scale encoders—essential for applications like robotic-assisted surgical procedures. Her research on optical fiber Bragg grating shape sensors, enhanced through deep learning, has enabled accurate 3D shape estimation of snake-like manipulators, a breakthrough for navigation in confined anatomical spaces. With over 44 citations across her key publications, including her 2020 encoder paper (16 citations) and 2023 deep learning study (14 citations), Freund’s impact is evident. She has also contributed to the MIRACLE project, developing the ASTRAS angular sensor for robotic laser osteotomies, demonstrating her commitment to translating research into clinical tools. Her work is notable for combining deep learning with optical sensing, pushing the boundaries of precision in robot-assisted surgery.

Research Focus

Key Achievements

Top Papers

1. 1
   Proof of concept of a novel absolute rotary encoder

   16 citations · 2020
2. 2
   Shape sensing of optical fiber Bragg gratings based on deep learning

   14 citations · 2023
3. 3
   Using supervised deep-learning to model edge-FBG shape sensors: a feasibility study

   6 citations · 2021
4. 4
   Data Acquisition System for a Medical Tracking Device based on a Novel Angular Sensor : How to Acquire and Process Data from the ASTRAS Tracking System

   4 citations · 2019
5. 5
   Preliminary Tests of the Miniaturization of a Novel Concept of Angular Sensors

   4 citations · 2019

Key Collaborators

Contact & Links