A Multimodal Feedback Sensing System for an Endovascular Surgery Robot

Robot-assisted endovascular surgery (RES) acts as a promising alternative to aid medical cures. To overcome the current limitations of single feedback sensing for surgeons in RES, in this work, a multimodal feedback sensing system integrated with four kinds of feedback (temperature, vibration, haptic, and vision) are developed, fabricated, and tested to provide immersive feedback sensing to surgeons, improve the transparency and safety for operations. A small-sized wearable sensing bracelet (WSB), assembled by Peltier module, vibration motor, and film thermistor, is designed to generate continuous real-time feedback sensing (thermal feedback and vibrating feedback) and measure the temperature of thermal feedback. The haptic interface can offer force feedback. The monitor window is able to provide visual feedback. Besides, a multilevel control strategy for the feedback sensing system is proposed to indicate different surgical conditions. The performance evaluation for three feedback and insertions (in vitro experiments) are carried out to verify the feasibility and repeatability. Insertion results by five subjects demonstrate that proposed multimodal feedback sensing system achieves fast response, immersive feedback, and large repetition capacity. Moreover, benefiting from the inspiration of multimodal feedback sensing, it is suitable to combine more feedback stimulus, even possible to explore newly immersive/spatial feedback sensing for leader-follower robotics.

Authors

Institutions

Publication Details

Journal
IEEE Sensors Journal
Published
2025-07-14
DOI
https://doi.org/10.1109/jsen.2025.3587324
Primary Topic
Soft Robotics and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Multimodal Feedback Sensing System for an Endovascular Surgery Robot

Masahiko Kawanishi, Shuxiang Guo, Xinming Li, Keisuke Suzuki
IEEE Sensors Journal
Soft Robotics and Applications
article

A Multimodal Feedback Sensing System for an Endovascular Surgery Robot

Masahiko Kawanishi, Shuxiang Guo, Xinming Li, Keisuke Suzuki
article en

Abstract

Robot-assisted endovascular surgery (RES) acts as a promising alternative to aid medical cures. To overcome the current limitations of single feedback sensing for surgeons in RES, in this work, a multimodal feedback sensing system integrated with four kinds of feedback (temperature, vibration, haptic, and vision) are developed, fabricated, and tested to provide immersive feedback sensing to surgeons, improve the transparency and safety for operations. A small-sized wearable sensing bracelet (WSB), assembled by Peltier module, vibration motor, and film thermistor, is designed to generate continuous real-time feedback sensing (thermal feedback and vibrating feedback) and measure the temperature of thermal feedback. The haptic interface can offer force feedback. The monitor window is able to provide visual feedback. Besides, a multilevel control strategy for the feedback sensing system is proposed to indicate different surgical conditions. The performance evaluation for three feedback and insertions (in vitro experiments) are carried out to verify the feasibility and repeatability. Insertion results by five subjects demonstrate that proposed multimodal feedback sensing system achieves fast response, immersive feedback, and large repetition capacity. Moreover, benefiting from the inspiration of multimodal feedback sensing, it is suitable to combine more feedback stimulus, even possible to explore newly immersive/spatial feedback sensing for leader-follower robotics.

IEEE Sensors JournalVol. 25(16)
Kagawa University (JP), Southern University of Science and Technology (CN), Takamatsu University (JP), Guangzhou Maritime College (CN), Guangdong Institute of Intelligent Manufacturing (CN), Shenzhen Technology University (CN), South China University of Technology (CN)
Openalex Percentile: Top 84%
Soft Robotics and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Multimodal Feedback Sensing System for an Endovascular Surgery Robot — Masahiko Kawanishi, Shuxiang Guo, et al. · IEEE Sensors Journal (2025) | TGRS Research Map | TGRS