Embodiment of supernumerary robotic limbs in virtual reality

Abstract

The supernumerary robotic limb system expands the motor function of human users by adding extra artificially designed limbs. It is important for us to embody the system as if it is a part of one's own body and to maintain cognitive transparency in which the cognitive load is suppressed. Embodiment studies have been conducted with an expansion of bodily functions through a "substitution" and "extension". However, there have been few studies on the "addition" of supernumerary body parts. In this study, we developed a supernumerary robotic limb system that operates in a virtual environment, and then evaluated whether the extra limb can be regarded as a part of one's own body using a questionnaire and whether the perception of peripersonal space changes with a visuotactile crossmodal congruency task. We found that the participants can embody the extra-limbs after using the supernumerary robotic limb system. We also found a positive correlation between the perceptual change in the crossmodal congruency task and the subjective feeling that the number of one's arms had increased (supernumerary limb sensation). These results suggest that the addition of an extra body part may cause the participants to feel that they had acquired a new body part that differs from their original body part through a functional expansion.

Figure 1

(a) The system diagram depicts the components of the system used in this experiment and their relationships. The solid line indicates a wired connection, and the dotted line indicates a wireless connection. (b) VR system calibration shows the correspondence between the real space and the VR space. (c) The first- and third-person perspectives of a ball touching task are shown. When the participant touches the ball, it vibrates to the location corresponding to the position of the innate foot. (d) An example of the visuotactile stimulus condition is a crossmodal congruency task (CCT). In this example, the tactile stimulus was transmitted to the back of the left foot, and the visual stimulus was presented to the back of the hand and palm of the left and right robot arms.

Figure 2

The duration time per set in the ball touch task. The circled markers are the results of the subjects, and the box plot shows the characteristics of the distribution of each set. *...$p<0.05$, **...$p<0.01$, ***...$p<0.001$.

Figure 3

IE-CCE score. The IE-CCE is displayed for each pre -/post-learning, and the results are compared for the ipsilateral and contralateral conditions. Error bars represent the variability among the subjects for each condition. Pairwise comparisons by adjusting p value for which a statistically significant difference ($adj.p<0.05$) can be confirmed are indicated by an asterisk (*). *...$adj.p<0.05$.

Figure 4

Embodiment Questionnaire Results. 7-Likert scales were used to collect responses to each question in each of the pre- and post-learning stages. Bar graphs are shown for each condition. The results are shown as boxplots for each condition. Asterisks (*) indicate statistically significant differences between the pre- and post-learning conditions. *...$p<0.05$, **...$p<0.01$, ***...$p<0.001$.

Figure 5

Correlation between the change in IE-CCE under the same-side condition and the change in response to the embodiment score Q3 before and after learning. Dots indicate points resampled by the bootstrap method, and linearly approximated lines and $95\%$ confidence intervals are shown as the shadows of the regression lines.

Figure 6

Correlation between the changes in response to the embodiment score Q1 and Q4 before and after learning. Dots indicate points resampled by the bootstrap method, and linearly approximated lines and $95\%$ confidence intervals are shown as shadows of the regression lines.