Meet the Motivational Robot That Predicts Your Future Feelings

This study explores the potential benefits of robots having the capability to anticipate people’s mental states in an exercise context. We designed 80 utterances for a robot with associated gestures that exhibit a range of emotional characteristics and then performed a 23-person data collection to investigate the effects of these robot behaviors on human mental states during exercise. The results of cluster analysis revealed that (1) utterances with similar meanings had the same effect and (2) the effects of a certain cluster on different people depend on their emotional state. On the basis of these findings, we proposed a robotic system that anticipates the effect of utterances on the individual’s future mental state, thereby choosing utterances that can positively impact the individual. This system incorporates three main features: (1) associating the relevant events detected by sensors with a user’s emotional state; (2) anticipating the effects of robot behavior on the user’s future mental state to choose the next behavior that maximizes the anticipated gain; and (3) determining appropriate times to provide coaching feedback, using predefined rules in the motion module for timing decisions. To evaluate the proposed system’s overall performance comprehensively, we compare robots equipped with the system’s unique features to those lacking these features. We design the baseline condition that lacks these unique features, opting for periodic random selection of utterances for interaction based on the current context. We conducted a 21-person experiment to evaluate the system’s performance. We found that participants perceived the robot to have a good understanding of their mental states and that they enjoyed the exercises more and put in more effort due to the robot’s encouragement.

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Publication Details

Journal
ACM Transactions on Human-Robot Interaction
Published
2024-12-06
DOI
https://doi.org/10.1145/3707466
Citations
2
Primary Topic
Social Robot Interaction and HRI
Type
article
Field-Weighted Citation Impact
1.29
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article

Meet the Motivational Robot That Predicts Your Future Feelings

Kurima Sakai, Takayuki Kanda, Takashi Minato, Rui Chen
2 citations
ACM Transactions on Human-Robot Interaction
Social Robot Interaction and HRI
1.29
article

Meet the Motivational Robot That Predicts Your Future Feelings

Kurima Sakai, Takayuki Kanda, Takashi Minato, Rui Chen
article en
2 citations

Abstract

This study explores the potential benefits of robots having the capability to anticipate people’s mental states in an exercise context. We designed 80 utterances for a robot with associated gestures that exhibit a range of emotional characteristics and then performed a 23-person data collection to investigate the effects of these robot behaviors on human mental states during exercise. The results of cluster analysis revealed that (1) utterances with similar meanings had the same effect and (2) the effects of a certain cluster on different people depend on their emotional state. On the basis of these findings, we proposed a robotic system that anticipates the effect of utterances on the individual’s future mental state, thereby choosing utterances that can positively impact the individual. This system incorporates three main features: (1) associating the relevant events detected by sensors with a user’s emotional state; (2) anticipating the effects of robot behavior on the user’s future mental state to choose the next behavior that maximizes the anticipated gain; and (3) determining appropriate times to provide coaching feedback, using predefined rules in the motion module for timing decisions. To evaluate the proposed system’s overall performance comprehensively, we compare robots equipped with the system’s unique features to those lacking these features. We design the baseline condition that lacks these unique features, opting for periodic random selection of utterances for interaction based on the current context. We conducted a 21-person experiment to evaluate the system’s performance. We found that participants perceived the robot to have a good understanding of their mental states and that they enjoyed the exercises more and put in more effort due to the robot’s encouragement.

ACM Transactions on Human-Robot InteractionVol. 14(2)
Kyoto University (JP), Advanced Telecommunications Research Institute International (JP), Guardian Industries (United States) (US)
Openalex Percentile: Top 17%
Social Robot Interaction and HRI
1.29
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