Measuring Surgeons’ Mental Workload With a Time-Based Secondary Task

Grant, Russell C.; Carswell, C. Melody; Lio, Cindy H.; Seales, W. Brent

doi:10.1177/1064804612466068

Cited by 10 publications

(7 citation statements)

References 9 publications

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“…Intraoperative Objective Non-interruptive Low-cost Questionnaire [10,26]- [29] × × × Expert annotation [14,30] × × Secondary-task [12,13] × Sensor-based [19,28,[31][32][33][34] most widely used questionnaires include NASA Task Load Index (NASA-TLX) [9], SURG-TLX [27], Subjective Workload Assessment Technique (SWAT) [10], and Workload Profile (WP) [29]. Some studies have implemented these surveys intraoperatively, but these surveys interrupt operative flow.…”

Section: Approachmentioning

confidence: 99%

“…In surgery, this may impacts patient safety, thus not feasible with live patients. In Reference [13], an interval production task was proposed as a secondary task to measure surgeons' mental workload. The interval production task required the subject to make a response each time when they believe a target time interval has elapsed (e.g., between 3 and 30 seconds).…”

Section: Secondary Tasks For Workload Assessmentmentioning

confidence: 99%

“…To provide user-driven autonomous assistance, we first need to reliably estimate surgeons' workload levels intraoperatively. Current approaches for measuring workload predominantly rely on post-task surveys [9][10][11][12][13][14]. Although surveys are cost-effective and can be easily implemented in hospitals, the usefulness of surveys in RAS is limited, since high workload states will only be identified post-surgery after the surgeons take the survey.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal Physiological Signals for Workload Prediction in Robot-assisted Surgery

Zhou

Cha

Gonzalez

et al. 2020

J. Hum.-Robot Interact.

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Monitoring surgeon workload during robot-assisted surgery can guide allocation of task demands, adapt system interfaces, and assess the robotic system's usability. Current practices for measuring cognitive load primarily rely on questionnaires that are subjective and disrupt surgical workflow. To address this limitation, a computational framework is demonstrated to predict user workload during telerobotic surgery. This framework leverages wireless sensors to monitor surgeons' cognitive load and predict their cognitive states. Continuous data across multiple physiological modalities (e.g., heart rate variability, electrodermal, and electroencephalogram activity) were simultaneously recorded for twelve surgeons performing surgical skills tasks on the validated da Vinci Skills Simulator. These surgical tasks varied in difficulty levels, e.g., requiring varying visual processing demand and degree of fine motor control. Collected multimodal physiological signals were fused using independent component analysis, and the predicted results were compared to the ground-truth workload level. Results compared performance of different classifiers, sensor fusion schemes, and physiological modality (i.e., prediction with single vs. multiple modalities). It was found that our multisensor approach outperformed individual signals and can correctly predict cognitive workload levels 83.2% of the time during basic and complex surgical skills tasks. CCS Concepts: • Human-centered computing → Human computer interaction (HCI); Interactive systems and tools; User interface management systems;

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Section: Approachmentioning

confidence: 99%

Section: Secondary Tasks For Workload Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multimodal Physiological Signals for Workload Prediction in Robot-assisted Surgery

Zhou

Cha

Gonzalez

et al. 2020

J. Hum.-Robot Interact.

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“…There is ample evidence from different fields of research that adding a secondary task to a primary task usually deteriorates performance in the primary task [4][5][6][7]. Controlled studies of driving behaviour have shown time and again that adding a task like using a mobile phone or navigation system to the driving task often leads to increased reaction times [e.g.…”

Section: Spoken Language Interfaces In Drivingmentioning

confidence: 99%

Can User-Paced, Menu-free Spoken Language Interfaces Improve Dual Task Handling While Driving?

Eriksson

Lindström

Seward

et al. 2014

Human-Computer Interaction. Advanced Interaction Modalities and Techniques

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Abstract. The use of speech-based interaction over traditional means of interaction in secondary tasks may increase safety in demanding environments with high requirements on operator attention. Speech interfaces have suffered from issues similar to those of visual displays, as they often rely on a complex menu structure that corresponds to that of visual systems. Recent advances in speech technology allow the use of natural language, eliminating the need for menu structures and offering a tighter coupling between the intention to act and the completion of the action. Modern speech technology may not only make already existing types of interaction safer, but also opens up for new applications, which may enhance safety. One such application is a speech-based hazard reporting system. A small fixed-base simulator study showed that drivers adapt the timing of the hazard reports to the situation at hand, such that an increase in reported workload was avoided.

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“…Notably, the task interfered with working memory, while also allowing participants to listen to the questions asked, close their eyes as instructed, orally respond to questions, and use their dominant hand to sketch. Time-estimation has been used as a secondary task in several studies on the mental workload associated with driving automobiles, piloting planes, conducting surgery, and other basic cognitive processes (Baldauf, Burgard, & Wittmann, 2009;Grant, Carswell, Lio, & Seales, 2013;Grant, Carswell, Lio, Seales, & Clark, 2009;Liu & Wickens, 1994).…”

Section: Working Memory Impairmentmentioning

confidence: 99%

Using the Cognitive Interview to Enhance Recall During Contact Tracing

Mosser¹

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Thank you, first and foremost, to my advisor, Jacki Evans, for her endless feedback and support during the dissertation process. I never took for granted the obvious care, time, and effort you put into fostering the best project possible. I'm sure many, many students for years to come will benefit, as I have, from your mentorship. To the rest of my committee, your guidance has been invaluable. Ron Fisher, through your mentorship, humor and general chutzpah I learned to think both intuitively and critically, and, of course, to question everything. Lindsay Malloy, you encouraged me to undertake this project and I will always be grateful for the example you set as a woman in science. Finally, Mary Jo Trepka, you never hesitated to answer any questions I had regarding a field for which I knew little, and undoubtedly increased the quality of the work. A huge thank you to my research assistants for their tireless efforts to get this work completed. I couldn't have done it without you. Thanks especially to Zoila Boza, Lazaro Nunez, Stephanie Padron, and Cynthia Weber. Thank you also to FIU's Department of Psychology, which supported this work through the Psychology Dissertation Year Fund and the Graduate Student Seed Fund Award. Finally, Mom and Dad, you always told me that my best effort would be enough. I hope this work makes you proud. Blake and Michael, thank you for always letting me know you were in my corner. Lee and Cathy, thank you for your words of encouragement and support-it meant the world to me. Allison, thanks for getting me through the final push. Melissa, you made grad school worth it. You got me through some of the toughest days (and we had a lot of fun doing it). Finally, Reid, you know how I feel. This is for you, and I couldn't have done it without you. vi

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Measuring Surgeons’ Mental Workload With a Time-Based Secondary Task

Cited by 10 publications

References 9 publications

Multimodal Physiological Signals for Workload Prediction in Robot-assisted Surgery

Multimodal Physiological Signals for Workload Prediction in Robot-assisted Surgery

Can User-Paced, Menu-free Spoken Language Interfaces Improve Dual Task Handling While Driving?

Using the Cognitive Interview to Enhance Recall During Contact Tracing

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