Embedded Force Sensor with Deep Transformation Calibration for Interventional Soft Robots.

Journal: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
PMID: 40039800

Abstract

A calibration method for gelatin-graphite-based soft sensors is proposed. This approach uses convolutional deep learning approaches that account for a sensor's non-linear behaviour and reduce noise amplification. This technique offers a smaller minimum detectable force than other approaches and is particularly useful in sensitive surgical scenarios. The best calibration (CQT) scheme provides high performance, with a Mean Absolute Error of ≤11.2 mN, and accurate force estimation, especially for forces below 400 mN of amplitude. This sensing principle and calibration method can revolutionize surgical procedures and capitalize on the benefits of soft robotics, potentially enhancing precision and reducing surgical trauma.

Authors

  • Navid Masoumi
  • Andres C Ramos
  • Tannaz Torkaman
  • Javad Dargahi
    Concordia University, Tactile Sensing and Medical Robotics Laboratory, Mechanical and Industrial Engineering Department, Montreal, Canada.
  • Jake Barralet
  • Liane S Feldman
    Department of Surgery, McGill University, Montreal, Quebec, Canada.
  • Amir Hooshiar
    Concordia University, Tactile Sensing and Medical Robotics Laboratory, Mechanical and Industrial Engineering Department, Montreal, Canada.

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