Evidence map›Paper›PMID 39487470›Full record

ArticleJournal of neuroengineering and rehabilitation2024

Immersive virtual reality for learning exoskeleton-like virtual walking: a feasibility study.

Antonio Rodríguez-Fernández, Alex van den Berg, Salvatore Luca Cucinella, Joan Lobo-Prat, Josep M Font-Llagunes, Laura Marchal-Crespo

Abstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Antonio Rodríguez-Fernández *Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Center for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, 08028, Spain. antonio.rodriguez.fernandez@upc.edu.
Alex van den Berg *Department of Cognitive Robotics, Delft University of Technology, Delft, 2628, The Netherlands. A.vandenBerg-2@tudelft.nl.
Salvatore Luca CucinellaDepartment of Cognitive Robotics, Delft University of Technology, Delft, 2628, The Netherlands.
Joan Lobo-PratABLE Human Motion, Barcelona, 08028, Spain.
Josep M Font-LlagunesBiomechanical Engineering Lab, Department of Mechanical Engineering and Research Center for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, 08028, Spain.
Laura Marchal-CrespoDepartment of Cognitive Robotics, Delft University of Technology, Delft, 2628, The Netherlands.

Funding

Agència de Gestió d'Ajuts Universitaris i de Recerca 2020 FI_B1 00195
6 · The paper itself

Abstract

purposeVirtual 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.

methodsIn 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.

resultsWe 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.

conclusionsOur 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. This research offers valuable insights for future developments in IVR to support individuals with sensorimotor disorders in improving the learning of walking with wearable exoskeletons.

Indexed as

Exoskeleton DeviceFeasibility StudiesVirtual RealityWalkingAdultFeedback, SensoryFemaleHumansLearningMaleYoung AdultAugmented feedbackHead-mounted displayImmersive virtual realityLower-limbMotor learningPerson perspectiveSpinal cord injuryVirtual realityVisual feedbackWalkingWearable exoskeleton

Identifiers

PMID39487470
PMCPMC11531127

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.