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

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Blended shared control utilizing online identification : Regulating grasping forces of a surrogate surgical grasper.

PURPOSE: Surgical robots are increasingly common, yet routine tasks such as tissue grasping remain potentially harmful with high occurrences of tissue crush injury due to the lack of force feedback from the grasper. This work aims to investigate whether a blended shared control framework which utilizes real-time identification of the object being grasped as part of the feedback may help address the prevalence of tissue crush injury in robotic surgeries. METHODS: This work tests the proposed shared control framework and tissue identification algorithm on a custom surrogate surgical robotic grasping setup. This scheme utilizes identification of the object being grasped as part of the feedback to regulate to a desired force. The blended shared control is arbitrated between human and an implicit force controller based on a computed confidence in the identification of the grasped object. The online identification is performed using least squares based on a nonlinear tissue model. Testing was performed on five silicone tissue surrogates. Twenty grasps were conducted, with half of the grasps performed under manual control and half of the grasps performed with the proposed blended shared control, to test the efficacy of the control scheme. RESULTS: The identification method resulted in an average of 95% accuracy across all time samples of all tissue grasps using a full leave-grasp-out cross-validation. There was an average convergence time of [Formula: see text] ms across all training grasps for all tissue surrogates. Additionally, there was a reduction in peak forces induced during grasping for all tissue surrogates when applying blended shared control online. CONCLUSION: The blended shared control using online identification more successfully regulated grasping forces to the desired target force when compared with manual control. The preliminary work on this surrogate setup for surgical grasping merits further investigation on real surgical tools and with real human tissues.

Low Back Pain: A Biomechanical Rationale Based on "Patterns" of Disc Degeneration.

STUDY DESIGN: A nonlinear finite element study of a lumbar spine with different "patterns" of multilevel intervertebral disc degeneration. OBJECTIVE: To determine how different patterns of multilevel disc degeneration influence the biomechanical behavior of the lumbar spine. SUMMARY OF BACKGROUND DATA: Because of the complex etiology of low back pain, it is often difficult to identify the specific factors that contribute to the symptoms of a particular patient. Disc degeneration is associated with the development of low back pain, but its presence is not always synonymous with symptoms. However, studies have suggested that "patterns" of disc degeneration may provide insight into such pain generation rather than the overall presence of degenerative changes. Specifically, individuals with contiguous multilevel disc degeneration have been shown to exhibit higher presence and severity of low back pain than patients with skipped-level disc degeneration (i.e., healthy discs located in between degenerated discs). METHODS: In this study, the biomechanical differences between these patterns were analyzed using a nonlinear finite element model of the lumbar spine. Thirteen separate "patterns" of disc degeneration were evaluated using the model and simulated under normal physiological loading conditions in each of the primary modes of spinal motion. RESULTS: The results showed that stresses and forces of the surrounding ligaments, facets, and pedicles at certain vertebral levels of the spine were generally lower in skipped-level disc degeneration cases than in the contiguous multilevel disc degenerations cases even when the skipped level contained more degenerated discs. CONCLUSION: To our knowledge, this is the first study to illustrate the biomechanics of specific patterns of disc degeneration of the lumbar spine. Using a multilevel disc degeneration model, our study provides insights as to why various patterns of disc degeneration throughout the lumbar spine may affect motion and soft tissue structures as well that may have bearing in the clinical pathway of pain generation. LEVEL OF EVIDENCE: N/A.