Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN, USA.
Simulation and Training Technology Center, Army Research Laboratory, Orlando, FL, USA.
Int J Comput Assist Radiol Surg. 2019 Jan;14(1):117-127. doi: 10.1007/s11548-018-1866-8. Epub 2018 Oct 4.
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.
本研究提出了一种估算技术以及相应的条件，仅使用后端传感器即可准确估算达芬奇手术工具的握力和颌角。
本研究在近端和远端的定制硬件上采集的数据集上使用人工神经网络作为回归估算器。通过一系列实验，我们测试了由于操作频率变化、使用相反的颌部和使用不同工具对估计准确性的影响。然后进行了一项案例研究，将我们的估计技术与两种合成组织替代品的材料响应曲线的直接测量进行了比较。
我们确定了产生准确估计所需的以下标准：在训练频率范围内操作、使用相同的颌部和使用相同的工具。在这些标准下，握力的平均均方根误差为 1.04mN m，颌角的平均均方根误差为 0.17 度。此外，在案例研究中应用这些标准得出的直接测量值落在我们的估计技术的 95%置信带内。
本研究提出了我们的估计技术以及重要的训练标准，以进一步完善有关握力估计的文献。我们提出了训练标准，以开始为用于握力估计的估计技术的适用性建立界限，最终将其转化为临床实践。
