Cognitive State Sensing

Cognitive State Sensing

Welcome to the Healthcare Ergonomics Analytics Lab (HEAL)!

Cognitive State Sensing

An Eye-Fixation Related Electroencephalography Technique for Predicting Situation Awareness: Implications for Driver State Monitoring Systems

Maintaining good SA in Level 3 automated vehicles is crucial to drivers' takeover performance when the automated system fails. A multimodal fusion approach that enables the analysis of the visual behavioral and cognitive processes of SA can facilitate real-time assessment of SA in future driver state monitoring systems.


Quantifying Workload and Stress in Intensive Care Unit Nurses: Preliminary Evaluation Using Continuous Eye-Tracking

Prior studies have employed workload scoring systems or accelerometer data to assess ICU nurses’ workload. This is the first naturalistic attempt to explore nurses’ mental workload using eye movement data.


Eye-Tracking Metrics Predict Perceived Workload in Robotic Surgical Skills Training

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.


Physiological Measurements of Situation Awareness: A Systematic Review

Across different environments and tasks, assessments of SA are often performed using techniques designed specifically to directly measure SA, such as SAGAT, SPAM, and/or SART. However, research suggests that indirect physiological sensing methods may also be capable of predicting SA. Currently, it is unclear which particular physiological approaches are sensitive to changes in SA.

Teaching

News

Team


Principal Investigator

Denny Yu

Denny Yu Associate Professor, Edwardson School of Industrial Engineering

Research Interests

Quantifying intraoperative workload

Developing patient factors-based workload models

Medical device design and usability testing

Wearable sensors for intelligent health systems


Graduate Students

Haozhi Chen

Haozhi Chen PhD Student, Edwardson School of Industrial Engineering

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Peiran Liu

Peiran Liu PhD Student, Edwardson School of Industrial Engineering

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Alejandra De La Torre Garcia

Alejandra De La Torre Garcia PhD Student, Edwardson School of Industrial Engineering

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Ryan Villarreal

Ryan Villarreal PhD Student, Edwardson School of Industrial Engineering

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Jingkun Wang

Jingkun Wang PhD Student, Edwardson School of Industrial Engineering

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Bowen Zheng

Bowen Zheng PhD Student, Edwardson School of Industrial Engineering

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Alumni

Mina Ostovari PhD IE · Assistant Professor, Binghamton University

Jackie Cha PhD IE · Assistant Professor, University of Wisconsin

Quang Dao PhD IE · NASA

Hamed Asadi PhD IE · Abbott

Denys Bulikhov PhD IE · Northrop Grumman

Jing Yang PhD IE · Assistant Professor, University at Buffalo

Guoyang Zhou PhD IE · Research Scientist, Amazon

Chiho Lim PhD IE · Postdoc, Purdue

Marian Obuseh PhD IE · Amazon

Ryan Villarreal MSIE · Current PhD Student

Jingkun Wang MSIE · Current PhD Student

We're looking for undergraduate and graduate students interested in advancing research at the interface of human factors and healthcare. Please email me or drop by my office.


Denny Yu, PhD
315 N. Grant Street
Grissom Hall Room 268
West Lafayette, IN 47907
Tel.: 765-49-47346
Fax: 765-49-47693
Email: dennyyu@purdue.edu