Grace Teo

Prodigy Lab

Education:

Ph.D. & M.Sc. in Applied Experimental and Human Factors Psychology

Contact:
Phone: (407) 882-1309
E-mail: gteo@ist.ucf.edu

Grace has a Ph.D. in Human Factors from the University of Central Florida and has work experience in both Industrial & Organizational Psychology and Human Factors. Before pursuing her graduate degree, she worked as a Research Psychologist in the civil service and a military research institute in Singapore. Her work in these positions involved the application of psychometrics in personnel assessment and selection, conducting research in organizational development, and research in human performance and vigilance. When she came to UCF, Grace expanded on this line of research to include decision making processes, individual differences, automation, and human-robot teaming. Her dissertation was on enhancing performance in a human robot team by managing workload through a closed-loop system. The research involves developing a workload model that is based on physiological workload measures. While completing her doctorate, Grace earned certifications in Usability Design, and in Data Mining with SAS. She is keen understand the role of data mining techniques and artificial intelligence in technology and  autonomous systems, as well as the impact of all this on human performance and behavior. Grace has presented her work at several conferences including the HFES, AHFE, APA, HCII, ESV conferences, and is published in peer-reviewed journals. 

Projects/Honors/Publications

Teo, G., Reinerman-Jones, L., Matthews, G., Szalma, J., Jentsch, F., & Hancock, P. (2018). Enhancing the effectiveness of human-robot teaming with a closed-loop system. Applied Ergonomics, 67, 91-103.

With technological developments in robotics and their increasing deployment, human-robot teams are set to be a mainstay in the future. To develop robots that possess teaming capabilities, such as being able to communicate implicitly, the present study implemented a closed-loop system. This system enabled the robot to provide adaptive aid without the need for explicit commands from the human teammate, through the use of multiple physiological workload measures. Such measures of workload vary in sensitivity and there is large inter-individual variability in physiological responses to imposed taskload. Workload models enacted via closed-loop system should accommodate such individual variability. The present research investigated the effects of the adaptive robot aid vs. imposed aid on performance and workload. Results showed that adaptive robot aid driven by an individualized workload model for physiological response resulted in greater improvements in performance compared to aid that was simply imposed by the system.