Contact Force Prediction for a Robotic Transesophageal Ultrasound Probe via Operating Torque Sensing

Yiping Xie, Xilong Hou, Hongbin Liu, James Housden, Kawal Rhode, Zeng Guang Hou, Shuangyi Wang*

*Corresponding author for this work

Research output: Contribution to conference typesPaperpeer-review

3 Citations (Scopus)
56 Downloads (Pure)


The advent of transesophageal ultrasound robots has provided a new idea to simplify relevant clinical procedures. However, the existing add-on robots often lack the ability to predict the contact force between the probe tip and the tissue. This makes the control of this robot under teleoperation lacking in tactile feedback and difficult to obtain effective safety. In this study, we propose a neural network-based internal resistance modeling method. Based on this, we experimentally calibrated the relationship between the tip contact force and handwheel torque through a self-learning idea. The experimental results show that a microcontroller-deployable lightweight neural network can achieve a good result on the fitting of the internal resistance, with its standard deviation being less than 3%. Moreover, a good linear correlation between the tip contact force and the handwheel torque was demonstrated in the case of passively applied forces. Independent experiments with actively applied forces further demonstrated the feasibility of the prediction method, especially in the forward bending process, with the prediction error mostly within 20% of the baseline force. Therefore, we believe that the proposed method has good potential to improve the safe use of transesophageal ultrasound robots.


Conference3rd International Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2022, held in Conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022


  • Contact force estimation
  • Continuum robot
  • Ultrasound robot


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