Conditions for reliable grip force and jaw angle estimation of da Vinci surgical tools

  • Trevor K. StephensEmail author
  • John J. O’Neill
  • Nathan J. Kong
  • Mark V. Mazzeo
  • Jack E. Norfleet
  • Robert M. Sweet
  • Timothy M. Kowalewski
Original Article



This work presents an estimation technique as well as corresponding conditions which are necessary to produce an accurate estimate of grip force and jaw angle on a da Vinci surgical tool using back-end sensors alone.


This work utilizes an artificial neural network as the regression estimator on a dataset acquired from custom hardware on the proximal and distal ends. Through a series of experiments, we test the effect of estimation accuracy due to change in operating frequency, using the opposite jaw, and using different tools. A case study is then presented comparing our estimation technique with direct measurements of material response curves on two synthetic tissue surrogates.


We establish the following criteria as necessary to produce an accurate estimate: operate within training frequency bounds, use the same side jaw, and use the same tool. Under these criteria, an average root mean square error of 1.04 mN m in grip force and 0.17 degrees in jaw angle is achieved. Additionally, applying these criteria in the case study resulted in direct measurements which fell within the 95% confidence bands of our estimation technique.


Our estimation technique, along with important training criteria, is presented herein to further improve the literature pertaining to grip force estimation. We propose the training criteria to begin establishing bounds on the applicability of estimation techniques used for grip force estimation for eventual translation into clinical practice.


Grip force estimation Surgical robotics Artificial neural network 



Research was sponsored in part by the Army Research Laboratory and was accomplished under Cooperative Agreement No. W911NF-14-2-0035. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the US Government. The US Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. Additionally, this material is based upon work supported in part by the National Science Foundation Graduate Research Fellowship under Grant No. 00039202. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This article does not contain patient data.


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Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of MinnesotaMinneapolisUSA
  2. 2.Simulation and Training Technology CenterArmy Research LaboratoryOrlandoUSA
  3. 3.Department of UrologyUniversity of WashingtonSeattleUSA

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