Abstract：
OBJECTIVE: This study aims predicting the stress level based on the ergonomic (kinematic) and physiological (electrodermal activity-EDA, blood pressure and body temperature) parameters of the surgeon from their records collected in the previously immediate situation of a minimally invasive robotic surgery activity.
METHODS: For this purpose, data related to the surgeon\'s ergonomic and physiological parameters were collected during twenty-six robotic-assisted surgical sessions completed by eleven surgeons with different experience levels. Once the dataset was generated, two preprocessing techniques were applied (scaled and normalized), these two datasets were divided into two subsets: with 80% of data for training and cross-validation, and 20% of data for test. Three predictive techniques (multiple linear regression-MLR, support vector machine-SVM and multilayer perceptron-MLP) were applied on training dataset to generate predictive models. Finally, these models were validated on cross-validation and test datasets. After each session, surgeons were asked to complete a survey of their feeling of stress. These data were compared with those obtained using predictive models.
RESULTS: The results showed that MLR combined with the scaled preprocessing achieved the highest R2 coefficient and the lowest error for each parameter analyzed. Additionally, the results for the surgeons\' surveys were highly correlated to the results obtained by the predictive models (R2 = 0.8253).
CONCLUSIONS: The linear models proposed in this study were successfully validated on cross-validation and test datasets. This fact demonstrates the possibility of predicting factors that help us to improve the surgeon\'s health during robotic surgery.
METHODS: For this purpose, data related to the surgeon\'s ergonomic and physiological parameters were collected during twenty-six robotic-assisted surgical sessions completed by eleven surgeons with different experience levels. Once the dataset was generated, two preprocessing techniques were applied (scaled and normalized), these two datasets were divided into two subsets: with 80% of data for training and cross-validation, and 20% of data for test. Three predictive techniques (multiple linear regression-MLR, support vector machine-SVM and multilayer perceptron-MLP) were applied on training dataset to generate predictive models. Finally, these models were validated on cross-validation and test datasets. After each session, surgeons were asked to complete a survey of their feeling of stress. These data were compared with those obtained using predictive models.
RESULTS: The results showed that MLR combined with the scaled preprocessing achieved the highest R2 coefficient and the lowest error for each parameter analyzed. Additionally, the results for the surgeons\' surveys were highly correlated to the results obtained by the predictive models (R2 = 0.8253).
CONCLUSIONS: The linear models proposed in this study were successfully validated on cross-validation and test datasets. This fact demonstrates the possibility of predicting factors that help us to improve the surgeon\'s health during robotic surgery.
摘要：
目的：本研究旨在根据人体工程学（运动学）和生理学（皮肤电活动-EDA，血压和体温）外科医生的参数来自他们在微创机器人手术活动的先前即时情况下收集的记录。
方法：为此，在由11名具有不同经验水平的外科医生完成的26次机器人辅助外科手术中,收集了与外科医生的人体工程学和生理参数相关的数据.一旦数据集生成，应用了两种预处理技术(缩放和归一化)，这两个数据集分为两个子集：80%的数据用于训练和交叉验证，和20%的数据用于测试。三种预测技术(多元线性回归-MLR，支持向量机-SVM和多层感知器-MLP）应用于训练数据集以生成预测模型。最后,这些模型在交叉验证和测试数据集上进行了验证.每次会议结束后,外科医生被要求完成对他们的压力感觉的调查。将这些数据与使用预测模型获得的数据进行比较。
结果：结果表明，MLR与缩放预处理相结合，对于所分析的每个参数，R2系数最高，误差最低。此外,外科医生的调查结果与预测模型的结果高度相关(R2=0.8253).
结论：本研究中提出的线性模型在交叉验证和测试数据集上成功验证。这一事实证明了预测因素的可能性，这些因素有助于我们在机器人手术期间改善外科医生的健康。
方法：为此，在由11名具有不同经验水平的外科医生完成的26次机器人辅助外科手术中,收集了与外科医生的人体工程学和生理参数相关的数据.一旦数据集生成，应用了两种预处理技术(缩放和归一化)，这两个数据集分为两个子集：80%的数据用于训练和交叉验证，和20%的数据用于测试。三种预测技术(多元线性回归-MLR，支持向量机-SVM和多层感知器-MLP）应用于训练数据集以生成预测模型。最后,这些模型在交叉验证和测试数据集上进行了验证.每次会议结束后,外科医生被要求完成对他们的压力感觉的调查。将这些数据与使用预测模型获得的数据进行比较。
结果：结果表明，MLR与缩放预处理相结合，对于所分析的每个参数，R2系数最高，误差最低。此外,外科医生的调查结果与预测模型的结果高度相关(R2=0.8253).
结论：本研究中提出的线性模型在交叉验证和测试数据集上成功验证。这一事实证明了预测因素的可能性，这些因素有助于我们在机器人手术期间改善外科医生的健康。
