Despite the increasing presence of social robots (SRs) in Human-Robot Interaction, there are few studies that quantify these interactions and explore children's attitudes by analyzing real-time data as they communicate with SRs. Therefore, we attempted to explore the interaction between pediatric patients and SRs by analyzing the interaction log collected from real-time. This study is a retrospective analysis of data collected in a prospective study conducted on 10 pediatric cancer patients at tertiary hospitals in Korea. Using the Wizard of Oz method, we collected the interaction log during the interaction between pediatric cancer patients and the robot. Out of the collected data, 955 sentences from the robot and 332 sentences from the children were available for analysis, except for the logs that were missing due to environmental errors. we analyzed the delay time from saving the interaction log and the sentence similarity of the interaction log. The interaction log delay time between robot and child was 5.01 seconds. And the child's delay time averaged 7.2 seconds, which was longer than the robot's delay time of 4.29 seconds. Additionally, as a result of analyzing the sentence similarity of the interaction log, the robot (97.2%) was higher than the children (46.2%). The results of the sentiment analysis of the patient's attitude toward the robot were 73% neutral, 13.59% positive, and 12.42% negative. The observational evaluations of pediatric psychological experts identified curiosity (n=7, 70.0%), activity (n=5, 50.0%), passivity (n=5, 50.0%), sympathy (n=7, 70.0%), concentration (n=6, 60.0%), high interest (n=5, 50.0%), positive attitude (n=9, 90.0%), and low interaction initiative (n=6, 60.0%). This study made it possible to explore the feasibility of interaction with SRs and to confirm differences in attitudes toward robots according to child characteristics. To increase the feasibility of human-robot interaction, measures such as improving the completeness of log records by enhancing the network environment are required.
Keywords: Pediatrics; dialog data analysis; human-robot interaction; real-time data; sentiment analysis.