arXiv subjects

Go-Eum Cha

Publications and source records attributed to Go-Eum Cha.

6 recordsLinked to original sources

arXiv2025

Motivating Students' Self-study with Goal Reminder and Emotional Support

While the efficacy of social robots in supporting people in learning tasks has been extensively investigated, their potential impact in assisting students in self-studying contexts has not been investigated much. This study explores how a social robot can act as a peer study companion for college students during self-study tasks by delivering task-oriented goal reminder and positive emotional support. We conducted an exploratory Wizard-of-Oz study to explore how these robotic support behaviors impacted students' perceived focus, productivity, and engagement in comparison to a robot that only provided physical presence (control). Our study results suggest that participants in the goal reminder and the emotional support conditions reported greater ease of use, with the goal reminder condition additionally showing a higher willingness to use the robot in future study sessions. Participants' satisfaction with the robot was correlated with their perception of the robot as a social other, and this perception was found to be a predictor for their level of goal achievement in the self-study task. These findings highlight the potential of socially assistive robots to support self-study through both functional and emotional engagement.

cs.RO↗

arXiv2025

A Robot That Listens: Enhancing Self-Disclosure and Engagement Through Sentiment-based Backchannels and Active Listening

As social robots get more deeply integrated intoour everyday lives, they will be expected to engage in meaningful conversations and exhibit socio-emotionally intelligent listening behaviors when interacting with people. Active listening and backchanneling could be one way to enhance robots' communicative capabilities and enhance their effectiveness in eliciting deeper self-disclosure, providing a sense of empathy,and forming positive rapport and relationships with people.Thus, we developed an LLM-powered social robot that can exhibit contextually appropriate sentiment-based backchannelingand active listening behaviors (active listening+backchanneling) and compared its efficacy in eliciting people's self-disclosurein comparison to robots that do not exhibit any of these listening behaviors (control) and a robot that only exhibitsbackchanneling behavior (backchanneling-only). Through ourexperimental study with sixty-five participants, we found theparticipants who conversed with the active listening robot per-ceived the interactions more positively, in which they exhibited the highest self-disclosures, and reported the strongest senseof being listened to. The results of our study suggest that the implementation of active listening behaviors in social robotshas the potential to improve human-robot communication andcould further contribute to the building of deeper human-robot relationships and rapport.

cs.HC↗

arXiv2023

Implications of Personality on Cognitive Workload, Affect, and Task Performance in Remote Robot Control

This paper explores how the personality traits of robot operators can influence their task performance during remote control of robots. It is essential to explore the impact of personal dispositions on information processing, both directly and indirectly, when working with robots on specific tasks. To investigate this relationship, we utilize the open-access multi-modal dataset MOCAS to examine the robot operator's personality traits, affect, cognitive load, and task performance. Our objective is to confirm if personality traits have a total effect, including both direct and indirect effects, that could significantly impact the performance levels of operators. Specifically, we examine the relationship between personality traits such as extroversion, conscientiousness, and agreeableness, and task performance. We conduct a correlation analysis between cognitive load, self-ratings of workload and affect, and quantified individual personality traits along with their experimental scores. The findings show that personality traits do not have a total effect on task performance.

cs.RO↗

arXiv2022

Correlation between Unconscious Mouse Actions and Human Cognitive Workload

Unconscious behaviors are one of the indicators of the human perception process from a psychological perspective. As a result of perception responses, hand gestures show behavioral responses from given stimuli. Mouse usages in Human-Computer Interaction (HCI) show hand gestures that individuals perceive information processing. This paper presents an investigation of the correlation between unconscious mouse actions and human cognitive workload. We extracted mouse behaviors from a Robot Operating System (ROS) file-based dataset that user responses are reproducible. We analyzed redundant mouse movements to complete a dual $n$-back game by solely pressing the left and right buttons. Starting from a hypothesis that unconscious mouse behaviors predict different levels of cognitive loads, we statistically analyzed mouse movements. We also validated mouse behaviors with other modalities in the dataset, including self-questionnaire and eye blinking results. As a result, we found that mouse behaviors that occur unconsciously and human cognitive workload correlate.

cs.HC↗

arXiv2020

ROSbag-based Multimodal Affective Dataset for Emotional and Cognitive States

This paper introduces a new ROSbag-based multimodal affective dataset for emotional and cognitive states generated using Robot Operating System (ROS). We utilized images and sounds from the International Affective Pictures System (IAPS) and the International Affective Digitized Sounds (IADS) to stimulate targeted emotions (happiness, sadness, anger, fear, surprise, disgust, and neutral), and a dual N-back game to stimulate different levels of cognitive workload. 30 human subjects participated in the user study; their physiological data was collected using the latest commercial wearable sensors, behavioral data was collected using hardware devices such as cameras, and subjective assessments were carried out through questionnaires. All data was stored in single ROSbag files rather than in conventional Comma-separated values (CSV) files. This not only ensures synchronization of signals and videos in a data set, but also allows researchers to easily analyze and verify their algorithms by connecting directly to this dataset through ROS. The generated affective dataset consists of 1,602 ROSbag files, and size of the dataset is about 787GB. The dataset is made publicly available. We expect that our dataset can be great resource for many researchers in the fields of affective computing, HCI, and HRI.

cs.CY↗

arXiv2020

A ROS-based Framework for Monitoring Human and Robot Conditions in a Human-Multi-robot Team

This paper presents a framework for monitoring human and robot conditions in human multi-robot interactions. The proposed framework consists of four modules: 1) human and robot conditions monitoring interface, 2) synchronization time filter, 3) data feature extraction interface, and 4) condition monitoring interface. The framework is based on Robot Operating System (ROS), and it supports physiological and behavioral sensors and devices and robot systems, as well as custom programs. Furthermore, it allows synchronizing the monitoring conditions and sharing them simultaneously. In order to validate the proposed framework, we present experiment results and analysis obtained from the user study where 30 human subjects participated and simulated robot experiments.

cs.RO↗