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
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

Nicholas Anton
PhD Student, Edwardson School of Industrial Engineering

Contact

anton5@purdue.edu

LinkedIn

Haozhi Chen
PhD Student, Edwardson School of Industrial Engineering

Contact

chen1809@purdue.edu

LinkedIn

Peiran Liu
PhD Student, Edwardson School of Industrial Engineering

Contact

liu3820@purdue.edu

LinkedIn

Poushali Ray
PhD Student, Edwardson School of Industrial Engineering

Contact

ray182@purdue.edu

LinkedIn

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

Contact

delator4@purdue.edu

LinkedIn

Ryan Villarreal
PhD Student, Edwardson School of Industrial Engineering

Contact

villar10@purdue.edu

LinkedIn

Jingkun Wang
PhD Student, Edwardson School of Industrial Engineering

Contact

jingkun@purdue.edu

LinkedIn

Bowen Zheng
PhD Student, Edwardson School of Industrial Engineering

Contact

zheng710@purdue.edu

LinkedIn

Alumni

Mina Ostovari
PhD IE, Assistant Professor at Binghamton University

Jackie Cha
PhD IE, Assistant Professor at 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 at University at Buffalo

Guoyang Zhou
PhD IE, Research Scientist, Amazon

Chiho Lim
PhD IE, Postdoc, Purdue

Marian Obuseh
PhD IE, Amazon

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