Guoyang Zhou
Purdue University, West Lafayette, Indiana, United States
Mannequin-based simulations are widely used to train novice nurses. However, current mannequins have no dynamic facial expressions, which decreases the mannequins' fidelity and impacts students' learning outcomes and experience. This study proposes a projection-based AR system for overlaying dynamic facial expressions on a mannequin and implements the system in a stroke simulation. Thirty-six undergraduate nursing students participated in the study and were equally divided into the control (without the system) and experimental group (with the system). The participants' gaze behavior, simulation performance, and subjective evaluation were measured. Results illustrated that the participants focused more on the face-animated mannequin than the traditional mannequin during the simulation. Nursing experts believed that the face-animated mannequin increased the participants' performance in recognizing deviations but decreased their performance in seeking additional information. Moreover, the participants reported that the face-animated mannequin was more interactive and helpful for performing appropriate assessments than the traditional mannequin.
https://dl.acm.org/doi/abs/10.1145/3491102.3517562
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)