Robottheory Fitness: GoBot’s Engagement Edge for Spurring Physical Activity in Young Children

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Abstract

Children around the world are growing more sedentary over time, which leads to considerable accompanying wellness challenges. Pilot results from our research group have shown that robots may offer something different or better than other developmentally appropriate toys when it comes to motivating physical activity. However, the foundations of this work involved larger-group interactions in which it was difficult to tease apart potential causes of motion, or one-time sessions during which the impact of the robot may have been due to novelty. Accordingly, the work in this paper covers more controlled interactions focused on one robot and one child participant, in addition to considering interactions over longitudinal observation. We discuss the results of a deployment during which N = 8 participants interacted with our custom GoBot robot over two months of weekly sessions. Within each session, the child users experienced a teleoperated robot mode, a semi-autonomous robot mode, and a control condition during which the robot was present but inactive. Results showed that children tended to be more active when the robot was active and the teleoperated mode did not yield significantly different results than the semi-autonomous mode. These insights can guide future application of assistive robots in child motor interventions, in addition to informing how these robots can be equipped to assist busy human clinicians.

Citation

Mayoral, Rafael Morales and Helmi, Ameer and Warren, Shel-Twon and Logan, Samuel W and Fitter, Naomi T. “Robottheory Fitness: GoBot’s Engagement Edge for Spurring Physical Activity in Young Children” Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

@inproceedings{mayoral2023robottheory,
  title={Robottheory Fitness: {GoBot}'s Engagement Edge for Spurring Physical Activity in Young Children},
  author={Mayoral, Rafael Morales and Helmi, Ameer and Warren, Shel-Twon and Logan, Samuel W and Fitter, Naomi T},
  booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={7939--7944},
  year={2023}
}