Papers
13
Total Citations
301
H-Index
7
About
Jackie Cha is a human factors and biomedical engineering researcher whose work sits at the intersection of surgical robotics, cognitive science, and sensor-based performance assessment. Her scholarship focuses primarily on understanding and measuring surgeon workload, expertise, and nontechnical skills during robot-assisted surgery — a domain where objective, real-time evaluation methods have historically lagged behind clinical need. Cha's most influential contribution, "Eye-Tracking Metrics Predict Perceived Workload in Robotic Surgical Skills Training" (2019, 131 citations), demonstrated that unobtrusive physiological signals could reliably capture cognitive demands during complex robotic procedures, offering a meaningful alternative to disruptive questionnaire-based approaches. This work laid the groundwork for her subsequent multimodal sensing research, including EEG-based neural monitoring during live surgeries and machine learning frameworks for jointly estimating workload, performance, and expertise simultaneously. Beyond individual surgeon assessment, Cha has extended her methodology to surgical teams, developing sensor-based communication and proximity metrics to objectively capture nontechnical skills — the social and cognitive competencies known to significantly influence patient outcomes. Her scoping reviews and stakeholder studies further contextualize adoption barriers facing robotic surgery systems. With over 290 cumulative citations, Cha's research is steadily shaping how surgical training and human-robot interaction are evaluated in operating rooms worldwide.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9Joint Surgeon Attributes Estimation in Robot-Assisted Surgery5 citations · 2018
- 10