RESUMO
PURPOSE: 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.
RESUMO
BACKGROUND: This study aims to implement a set of wearable technologies to record and analyze the surgeon's physiological and ergonomic parameters during the performance of conventional and robotic-assisted laparoscopic surgery, comparing the ergonomics and stress levels of surgeons during surgical procedures. METHODS: This study was organized in two different settings: simulator tasks and experimental model surgical procedures. The participating surgeons performed the tasks and surgical procedures in both laparoscopic and robotic-assisted surgery in a randomized fashion. Different wearable technologies were used to record the surgeons' posture, muscle activity, electrodermal activity and electrocardiography signal during the surgical practice. RESULTS: The simulator study involved six surgeons: three experienced (>100 laparoscopic procedures performed; 36.33 ± 13.65 years old) and three novices (<100 laparoscopic procedures; 29.33 ± 8.39 years old). Three surgeons of different surgical specialties with experience in laparoscopic surgery (>100 laparoscopic procedures performed; 37.00 ± 5.29 years old), but without experience in surgical robotics, participated in the experimental model study. The participating surgeons showed an increased level of stress during the robotic-assisted surgical procedures. Overall, improved surgeon posture was obtained during robotic-assisted surgery, with a reduction in localized muscle fatigue. CONCLUSIONS: A set of wearable technologies was implemented to measure and analyze surgeon physiological and ergonomic parameters. Robotic-assisted procedures showed better ergonomic outcomes for the surgeon compared to conventional laparoscopic surgery. Ergonomic analysis allows us to optimize surgeon performance and improve surgical training.
