Purpose: Virtual Reality (VR) has proven to be an effective tool for motor (re)learning. Furthermore, with the current commercialization of low-cost head-mounted displays (HMDs), immersive virtual reality (IVR) has become a viable rehabilitation tool. Nonetheless, it is still an open question how immersive virtual environments should be designed to enhance motor learning, especially to support the learning of complex motor tasks. An example of such a complex task is triggering steps while wearing lower-limb exoskeletons as it requires the learning of several sub-tasks, e.g., shifting the weight from one leg to the other, keeping the trunk upright, and initiating steps. This study aims to find the necessary elements in VR to promote motor learning of complex virtual gait tasks. Methods: In this study, we developed an HMD-IVR-based system for training to control wearable lower-limb exoskeletons for people with sensorimotor disorders. The system simulates a virtual walking task of an avatar resembling the sub-tasks needed to trigger steps with an exoskeleton. We ran an experiment with forty healthy participants to investigate the effects of first- (1PP) vs. third-person perspective (3PP) and the provision (or not) of concurrent visual feedback of participants’ movements on the walking performance – namely number of steps, trunk inclination, and stride length –, as well as the effects on embodiment, usability, cybersickness, and perceived workload. Results: We found that all participants learned to execute the virtual walking task. However, no clear interaction of perspective and visual feedback improved the learning of all sub-tasks concurrently. Instead, the key seems to lie in selecting the appropriate perspective and visual feedback for each sub-task. Notably, participants embodied the avatar across all training modalities with low cybersickness levels. Still, participants’ cognitive load remained high, leading to marginally acceptable usability scores. Conclusions: Our findings suggest that to maximize learning, users should train sub-tasks sequentially using the most suitable combination of person’s perspective and visual feedback for each sub-task.

@en

In recent years, providing additional visual feedback about the interaction forces has been found to offer benefits to haptic-assisted teleoperation. However, there is limited insight into the effects of the design of force feedback-related visual cues and the type of visual display on the performance of teleoperation of robotic arms executing industrial tasks. In this study, we provide new insights into this interaction by extending these findings to the haptic assistance teleoperation of a simulated robotic arm in a virtual environment, in which the haptic assistance is comprised of a set of virtual fixtures. We design a novel method for providing visual cues about the interaction forces to complement the haptic assistance and augment visual feedback in virtual reality with a head-mounted display. We evaluate the visual cues method and head-mounted display method through human factors experiments in a teleoperated dross removal use case. The results show that both methods are beneficial for task performance, each of them having stronger points in different aspects of the operation. The visual cues method was found to significantly improve safety in terms of peak collision force, whereas the head-mounted display additionally improves the performance significantly. Furthermore, positive scores of the subjective analysis indicate an increased user acceptance of both methods. This work provides a new study on the importance of visual feedback related to (interaction) forces and spatial information for haptic assistance and provides two methods to take advantage of its potential benefits in the teleoperation of robotic arms.@en

Immersive Virtual Reality (IVR) has gained popularity in neurorehabilitation for its potential to increase patients’ motivation and engagement. A crucial yet relatively unexplored aspect of IVR interfaces is the patients’ representation in the virtual world, such as with an avatar. A higher level of embodiment over avatars has been shown to enhance motor performance during upper limb training and has the potential to be employed to enhance neurorehabilitation. However, the relationship between avatar embodiment and gait performance remains unexplored. In this work, we present the results of a pilot study with 12 healthy young participants that evaluates the effect of different virtual lower limb representations on foot placement accuracy while stepping over a trail of 16 virtual targets. We compared three levels of virtual representation: i) a full-body avatar, ii) only feet, and iii) no representation. Full-body tracking is computed using standard VR trackers to synchronize the avatar with the participants’ motions. Foot placement accuracy is measured as the distance between the foot’s center of mass and the center of the selected virtual target. Additionally, we evaluated the level of embodiment over each virtual representation through a questionnaire. Our findings indicate that foot placement accuracy increases with some form of virtual representation, either full-body or foot, compared to having no virtual representation. However, the foot and full-body representations do not show significant differences in accuracy. Importantly, we found a negative correlation between the level of embodiment of the foot representation and the distance between the placed foot and the target. However, no such correlation was found for the full-body representation. Our results highlight the importance of embodying a virtual representation of the foot when performing a task that requires accurate foot placement. However, showing a full-body avatar does not appear to further enhance accuracy. Moreover, our results suggest that the level of embodiment of the virtual feet might modulate motor performance in this stepping task. This work motivates future research on the effect of embodiment over virtual representations on motor control to be exploited for IVR gait rehabilitation.@en