Determining the Significant Kinematic Features for Characterizing Stress during Surgical Tasks Using Spatial Attention
Determining the Significant Kinematic Features for Characterizing Stress during Surgical Tasks Using Spatial Attention
复制标题
使用空间注意力确定手术任务期间表征应力的重要运动学特征
DOI：
10.1142/s2424905x22410069
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Majewicz Fey, Ann
中科院分区：
文献类型：
--
作者：
Zheng, Yi;Leonard, Grey;Zeh, Herbert;Majewicz Fey, Ann
已经表明，术中应力可能对腹腔镜手术期间外科医生的手术技能产生负面影响。对于新手外科医生，压力条件可能导致手术器械尖端的速度、加速度和急动度显著更高，从而导致更快但不太平滑的移动。然而，仍然不清楚这些运动学特征（速度、加速度或加加速度）中的哪一个是识别正常和应力条件的最佳标记。因此，为了找到受术中应力影响的最显著的运动学特征，我们实现了基于空间注意力的长短期记忆（LSTM）分类器。在先前IRB批准的实验中，我们收集了来自执行扩展挂钩转移任务的医学生的数据，这些医学生被随机分为对照组和在外部心理压力下执行任务的组。在我们之前的工作中，我们使用运动学数据作为输入，从该数据集获得了“代表性”正常或应力运动。在这项研究中，空间注意机制被用来描述每个运动学特征的正常/应激运动的分类的贡献。我们在Leave-One-User-Out（LOUO）交叉验证下测试了我们的分类器，该分类器使用运动学特征作为输入对“代表性”正常和应激运动进行分类的总体准确率达到77.11%。更重要的是，我们还研究了从所提出的分类器中提取的空间注意力。两侧的速度和加速度对正常动作的分类注意显著更高（）;非优势手的速度（）和加加速度（）对应激动作的分类注意显著更高，值得注意的是，从描述正常动作到描述应激动作时，非优势手一侧加加速度的注意增量最大（）。总体而言，我们发现非优势手侧的挺举可以更有效地表征新手外科医生的应激动作。
It has been shown that intraoperative stress can have a negative effect on surgeon surgical skills during laparoscopic procedures. For novice surgeons, stressful conditions can lead to significantly higher velocity, acceleration, and jerk of the surgical instrument tips, resulting in faster but less smooth movements. However, it is still not clear which of these kinematic features (velocity, acceleration, or jerk) is the best marker for identifying the normal and stressed conditions. Therefore, in order to find the most significant kinematic feature that is affected by intraoperative stress, we implemented a spatial attention-based Long Short-Term Memory (LSTM) classifier. In a prior IRB approved experiment, we collected data from medical students performing an extended peg transfer task who were randomized into a control group and a group performing the task under external psychological stresses. In our prior work, we obtained “representative” normal or stressed movements from this dataset using kinematic data as the input. In this study, a spatial attention mechanism is used to describe the contribution of each kinematic feature to the classification of normal/stressed movements. We tested our classifier under Leave-One-User-Out (LOUO) cross-validation, and the classifier reached an overall accuracy of 77.11% for classifying “representative” normal and stressed movements using kinematic features as the input. More importantly, we also studied the spatial attention extracted from the proposed classifier. Velocity and acceleration on both sides had significantly higher attention for classifying a normal movement (); Velocity () and jerk () on nondominant hand had significant higher attention for classifying a stressed movement, and it is worthy noting that the attention of jerk on nondominant hand side had the largest increment when moving from describing normal movements to stressed movements (). In general, we found that the jerk on nondominant hand side can be used for characterizing the stressed movements for novice surgeons more effectively.