School of Industrial Engineering, Purdue University, West Lafayette, Indiana, United States of America.
Goodman Campbell Brain and Spine, Indianapolis, Indiana, United States of America.
PLoS One. 2018 Jun 12;13(6):e0198092. doi: 10.1371/journal.pone.0198092. eCollection 2018.
Gestural interfaces allow accessing and manipulating Electronic Medical Records (EMR) in hospitals while keeping a complete sterile environment. Particularly, in the Operating Room (OR), these interfaces enable surgeons to browse Picture Archiving and Communication System (PACS) without the need of delegating functions to the surgical staff. Existing gesture based medical interfaces rely on a suboptimal and an arbitrary small set of gestures that are mapped to a few commands available in PACS software. The objective of this work is to discuss a method to determine the most suitable set of gestures based on surgeon's acceptability. To achieve this goal, the paper introduces two key innovations: (a) a novel methodology to incorporate gestures' semantic properties into the agreement analysis, and (b) a new agreement metric to determine the most suitable gesture set for a PACS.
Three neurosurgical diagnostic tasks were conducted by nine neurosurgeons. The set of commands and gesture lexicons were determined using a Wizard of Oz paradigm. The gestures were decomposed into a set of 55 semantic properties based on the motion trajectory, orientation and pose of the surgeons' hands and their ground truth values were manually annotated. Finally, a new agreement metric was developed, using the known Jaccard similarity to measure consensus between users over a gesture set.
A set of 34 PACS commands were found to be a sufficient number of actions for PACS manipulation. In addition, it was found that there is a level of agreement of 0.29 among the surgeons over the gestures found. Two statistical tests including paired t-test and Mann Whitney Wilcoxon test were conducted between the proposed metric and the traditional agreement metric. It was found that the agreement values computed using the former metric are significantly higher (p < 0.001) for both tests.
This study reveals that the level of agreement among surgeons over the best gestures for PACS operation is higher than the previously reported metric (0.29 vs 0.13). This observation is based on the fact that the agreement focuses on main features of the gestures rather than the gestures themselves. The level of agreement is not very high, yet indicates a majority preference, and is better than using gestures based on authoritarian or arbitrary approaches. The methods described in this paper provide a guiding framework for the design of future gesture based PACS systems for the OR.
手势界面允许在保持完全无菌环境的情况下访问和操作医院中的电子病历 (EMR)。特别是在手术室 (OR) 中，这些界面使外科医生能够浏览图片存档和通信系统 (PACS)，而无需将功能委托给手术人员。现有的基于手势的医学接口依赖于一组不理想且任意小的手势，这些手势映射到 PACS 软件中可用的少数命令。这项工作的目的是讨论一种基于外科医生可接受性确定最合适的手势集的方法。为了实现这一目标，本文介绍了两个关键创新：(a) 将手势的语义属性纳入协议分析的新方法，以及 (b) 用于确定 PACS 最合适的手势集的新协议度量。
由 9 名神经外科医生进行了 3 项神经外科诊断任务。使用 Wizard of Oz 范式确定命令和手势词汇表。根据外科医生手部的运动轨迹、方向和姿势将手势分解为一组 55 个语义属性，并手动注释其真实值。最后，开发了一种新的协议度量，使用已知的 Jaccard 相似性来衡量手势集上用户之间的共识。
发现 34 个 PACS 命令足以进行 PACS 操作。此外，还发现外科医生对手势的一致性程度为 0.29。进行了配对 t 检验和曼惠特尼威尔科克森检验两种统计检验，以比较所提出的度量和传统的一致性度量。发现使用前一种度量计算的一致性值在两种检验中都显著更高（p < 0.001）。
这项研究表明，外科医生对用于 PACS 操作的最佳手势的一致性程度高于之前报告的度量（0.29 对 0.13）。这一观察结果基于以下事实：协议侧重于手势的主要特征，而不是手势本身。一致性程度不是很高，但表示大多数人倾向于使用，并且优于使用基于权威或任意方法的手势。本文描述的方法为设计用于 OR 的未来基于手势的 PACS 系统提供了指导框架。
