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Robotic Stroke Motion Following the Shape of the Human Back: Motion Generation and Psychological Effects

Akishige Yuguchi, Tomoki Ishikura, Sung-Gwi Cho, Jun Takamatsu, Tsukasa Ogasawara

TL;DR

This work tackles robot assisted back massage by enabling a robotic stroke that follows the contour of the human back. It introduces a depth-camera based trajectory generation method that fits a cubic curve $ $z = a y^3 + b y^2 + c y + d$ to back shape data, derives waypoints, and computes end-effector orientation via $ $\theta_{\text{target}_x} = \tan^{-1}\frac{z_i - z_{i-1}}{y_i - y_{i-1}}$, with subsequent planning in MoveIt. Experimental results show the robotic trajectory achieves an average angular error of $ $\bar{\theta}_{\text{error}_x} = 5.97^{\circ}$ and a maximum of $8.26^{\circ}$, which is close to human back-stroke trajectories (averages around $4.78^{\circ}$ and $5.52^{\circ}$, max $7.30^{\circ}$ and $8.13^{\circ}$), indicating the method can closely mimic human strokes. Subjective evaluations using Affect Grid reveal that shape-following strokes tend to be more pleasant and arousing than linear strokes, especially at medium speed, suggesting potential benefits for robot-assisted massage and dementia care. Limitations include the reliance on a fixed cubic model, a single stroke direction, and a small participant pool, pointing to future work on real-time trajectory adaptation and broader body coverage.

Abstract

In this study, to perform the robotic stroke motions following the shape of the human back similar to the stroke motions by humans, in contrast to the conventional robotic stroke motion with a linear trajectory, we propose a trajectory generation method for a robotic stroke motion following the shape of the human back. We confirmed that the accuracy of the method's trajectory was close to that of the actual stroking motion by a human. Furthermore, we conducted a subjective experiment to evaluate the psychological effects of the proposed stroke motion in contrast to those of the conventional stroke motion with a linear trajectory. The experimental results showed that the actual stroke motion following the shape of the human back tended to evoke more pleasant and active feelings than the conventional stroke motion.

Robotic Stroke Motion Following the Shape of the Human Back: Motion Generation and Psychological Effects

TL;DR

This work tackles robot assisted back massage by enabling a robotic stroke that follows the contour of the human back. It introduces a depth-camera based trajectory generation method that fits a cubic curve z = a y^3 + b y^2 + c y + d , with subsequent planning in MoveIt. Experimental results show the robotic trajectory achieves an average angular error of \bar{\theta}_{\text{error}_x} = 5.97^{\circ}8.26^{\circ}4.78^{\circ}5.52^{\circ}7.30^{\circ}8.13^{\circ}$), indicating the method can closely mimic human strokes. Subjective evaluations using Affect Grid reveal that shape-following strokes tend to be more pleasant and arousing than linear strokes, especially at medium speed, suggesting potential benefits for robot-assisted massage and dementia care. Limitations include the reliance on a fixed cubic model, a single stroke direction, and a small participant pool, pointing to future work on real-time trajectory adaptation and broader body coverage.

Abstract

In this study, to perform the robotic stroke motions following the shape of the human back similar to the stroke motions by humans, in contrast to the conventional robotic stroke motion with a linear trajectory, we propose a trajectory generation method for a robotic stroke motion following the shape of the human back. We confirmed that the accuracy of the method's trajectory was close to that of the actual stroking motion by a human. Furthermore, we conducted a subjective experiment to evaluate the psychological effects of the proposed stroke motion in contrast to those of the conventional stroke motion with a linear trajectory. The experimental results showed that the actual stroke motion following the shape of the human back tended to evoke more pleasant and active feelings than the conventional stroke motion.
Paper Structure (13 sections, 2 equations, 5 figures)

This paper contains 13 sections, 2 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Trajectory Generation for Robotic Stroke Motion Following the Shape of the Human Back
  3. Generation of Robotic Stroke Motion and Its Trajectory Evaluation
  4. Trajectory and Motion Generation
  5. Evaluation
  6. Comparison with Stroke Motion Trajectory to the Back by Humans
  7. Measurement of Stroke Motions by Humans
  8. Measurement Result
  9. Subjective Experiment
  10. Method
  11. Result
  12. Discussion
  13. Conclusion

Figures (5)

  • Figure 1: Actual camera and depth images of the back.
  • Figure 2: The actual robotic stroke motion and the measured normals on the back and robot's end effector.
  • Figure 3: The actual human stroke motion and the measured normals on the back and hand.
  • Figure 4: The actual experimental setup
  • Figure 5: The result of subjective evaluation using Affect Grid ($*$: p $< 0.05$). Note that the condition non-linear trajectory is the stroke motion following the shape of the human back with the proposed method.