Objective: The aim of this study is to assess the relationship between eye-tracking measures and perceived workload in robotic surgical tasks. Background: Robotic techniques provide improved dexterity, stereoscopic vision, and ergonomic control system over laparoscopic surgery, but the complexity of the interfaces and operations may pose new challenges to surgeons and compromise patient safety. Limited studies have objectively quantified workload and its impact on performance in robotic surgery. Although not yet implemented in robotic surgery, minimally intrusive and continuous eye-tracking metrics have been shown to be sensitive to changes in workload in other domains. Methods: Eight surgical trainees participated in 15 robotic skills simulation sessions. In each session, participants performed up to 12 simulated exercises. Correlation and mixed-effects analyses were conducted to explore the relationships between eye-tracking metrics and perceived workload. Machine learning classifiers were used to determine the sensitivity of differentiating between low and high workload with eye-tracking features. Results: Gaze entropy increased as perceived workload increased, with a correlation of .51. Pupil diameter and gaze entropy distinguished differences in workload between task difficulty levels, and both metrics increased as task level difficulty increased. The classification model using eye-tracking features achieved an accuracy of 84.7% in predicting workload levels. Conclusion: Eye-tracking measures can detect perceived workload during robotic tasks. They can potentially be used to identify task contributors to high workload and provide measures for robotic surgery training. Application: Workload assessment can be used for real-time monitoring of workload in robotic surgical training and provide assessments for performance and learning.
Background Prior efforts to understand faculty culture have largely described monoliths where individuals are differentiated by their productivity. Little prior work provides rich faculty subcultural descriptions and their connections to specific activities, including disposition to change. Purpose/Hypothesis This article describes the goals, assumptions, methods, and inferences made about faculty culture within an engineering department at a large university with very high research activity, with the potential to enrich future discussions about change among the target audience of engineering faculty, administrators, and researchers. Design/Method We employ cultural consensus theory (CCT) to characterize faculty culture, based upon a detailed survey, analysis, and member checking. We use the academic ratchet—as a theoretical framework to interpret CCT results, and extend our understanding using previously published change theories. Results We discovered two faculty subcultures of roughly equal membership: (a) change‐oriented and (b) continuity‐embracing. Members of each subculture agree on the primacy of research but differ in their views of change, leadership, and trust. Members of the change‐oriented subculture seek large‐scale changes but feel disempowered to pursue them, while members of the continuity‐embracing subculture seek modest changes and feel empowered to enact them. Conclusions We introduce a scalable, person‐centered culture characterization approach (CCT) to the engineering education research community. This approach deepens our understanding of faculty culture, and our results reinforce the central role of the academic ratchet in shaping faculty activities. This analysis illustrates the potential roles of each subculture in enacting change of various types and magnitudes.
Proper communication is key to the adoption and implementation of differential privacy (DP). However, a prior study found that laypeople did not understand the dataperturbation processes of DP and how DP noise protects their sensitive personal information. Consequently, they distrusted the techniques and chose to opt out of participating.In this project, we designed explanative illustrations of three DP models (Central DP, Local DP, Shuffler DP) to help laypeople conceptualize how random noise is added to protect individuals' privacy and preserve group utility. Following pilot surveys and interview studies, we conducted two online experiments (N = 595) examining participants' comprehension, privacy and utility perception, and data-sharing decisions across the three DP models. Besides the comparisons across the three models, we varied the noise levels of each model. We found that the illustrations can be effective in communicating DP to the participants. Given an adequate comprehension of DP, participants preferred strong privacy protection for a certain type of data usage scenarios (i.e., commercial interests) at both the model level and the noise level. We also obtained empirical evidence showing participants' acceptance of the Shuffler DP model for data privacy protection. Our findings have implications for multiple stakeholders for user-centered deployments of differential privacy, including app developers, DP model developers, data curators, and online users.
Significant growth in the number of autonomous vehicles is expected in the coming years. With this technology, drivers will likely begin to disengage from the driving task and often experience mind wandering. Research has examined the effects of mind wandering on manual driving performance, but little work has been done to understand its impact on autonomous driving. In addition, it is unclear what physiological measurements can reveal about mind wandering in the driving context. Therefore, the goals of this paper were to (a) understand how mind wandering affects warning signal detection, semi-autonomous driving performance, and physiological responses, and (b) develop a model to predict mind wandering. Preliminary findings suggest that mind wandering may be observed as a result of road familiarity, and that the number of driving years and response times to alerts may be suitable predictors of mind wandering. This work is expected to help inform the design of future autonomous vehicles to prevent distracted driving behaviors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.