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Older Adults' Preferences for Feedback Cadence from an Exercise Coach Robot

Roshni Kaushik, Reid Simmons

TL;DR

This study addresses how cadence in verbal and nonverbal feedback from a robot exercise coach influences older adults' perceptions. It combines real-time exercise evaluation with a multimodal feedback controller and an online video-based 6-condition design to test different cadence levels, analyzing responses via repeated-measures ANOVA. The findings reveal significant main and cross-modal effects: higher verbal cadence enhances perceived clarity and helpfulness, and medium nonverbal cadence improves timeliness and engagement, with verbal cadences also shaping nonverbal perception and vice versa. The work provides evidence to inform personalized feedback cadence in robot-assisted exercise for older adults, though it is limited by its video-based methodology and calls for in-person validation and adaptive future designs.

Abstract

People can respond to feedback and guidance in different ways, and it is important for robots to personalize their interactions and utilize verbal and nonverbal communication cues. We aim to understand how older adults respond to different cadences of verbal and nonverbal feedback of a robot exercise coach. We conducted an online study of older adults, where participants evaluated videos of the robot giving feedback at different cadences for each modality. The results indicate that changing the cadence of one modality affects the perception of both it and the other modality. We can use the results from this study to better design the frequency of the robot coach's feedback during an exercise session with this population.

Older Adults' Preferences for Feedback Cadence from an Exercise Coach Robot

TL;DR

This study addresses how cadence in verbal and nonverbal feedback from a robot exercise coach influences older adults' perceptions. It combines real-time exercise evaluation with a multimodal feedback controller and an online video-based 6-condition design to test different cadence levels, analyzing responses via repeated-measures ANOVA. The findings reveal significant main and cross-modal effects: higher verbal cadence enhances perceived clarity and helpfulness, and medium nonverbal cadence improves timeliness and engagement, with verbal cadences also shaping nonverbal perception and vice versa. The work provides evidence to inform personalized feedback cadence in robot-assisted exercise for older adults, though it is limited by its video-based methodology and calls for in-person validation and adaptive future designs.

Abstract

People can respond to feedback and guidance in different ways, and it is important for robots to personalize their interactions and utilize verbal and nonverbal communication cues. We aim to understand how older adults respond to different cadences of verbal and nonverbal feedback of a robot exercise coach. We conducted an online study of older adults, where participants evaluated videos of the robot giving feedback at different cadences for each modality. The results indicate that changing the cadence of one modality affects the perception of both it and the other modality. We can use the results from this study to better design the frequency of the robot coach's feedback during an exercise session with this population.
Paper Structure (10 sections, 2 figures)

This paper contains 10 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Exercise Evaluation
  5. Robot Feedback
  6. Verbal Feedback
  7. Nonverbal Feedback
  8. Study Design
  9. Results and Discussion
  10. Conclusion

Figures (2)

  • Figure 1: Screenshot for a video from the study with the human exerciser on the left and the robot providing verbal and nonverbal feedback on the right.
  • Figure 2: Results of all Likert-style measures with perceptions of verbal feedback in the first row and nonverbal feedback in the second row. Significant differences are marked with one asterisk ($p<0.05$) or two asterisks ($p<0.01$). Each box plot shows the interquartile range of the data, as well as the min and max of the data. The mean is shown with a green triangle.