Virtual, Augmented, and Mixed Reality for Motor Neurorehabilitation: Scoping Review Focused on the Role of Body Representation
Notice bibliographique
Résumé
Background: Extended reality (XR), encompassing virtual reality, augmented reality (AR), and mixed reality, is increasingly being used in neurorehabilitation to provide multisensory feedback and promote neural plasticity in sensorimotor networks. Objective: This scoping review aimed to (1) examine how XR technologies are applied in motor neurorehabilitation, (2) explore how body representation and somatic embodiment are addressed, and (3) analyze the methodological designs of XR-based interventions. Methods: This review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, with a comprehensive search across PubMed, Embase, Scopus, and Web of Science from inception to December 2023. Eligible studies included original research involving XR-based interventions explicitly targeting neurorehabilitation. Studies related to somatic embodiment and reporting data on implementation and user outcomes were considered without date restriction. Three independent reviewers conducted screening in Covidence. The following variables were extracted: study design, participant characteristics, XR devices and software, experimentation details, treatment approaches, and evaluation methods. Methodological quality of the included studies was assessed using the Newcastle-Ottawa Scale and the Murad Scale. Findings have been presented in tabular and narrative formats. Results: Twenty-six studies met the inclusion criteria, and these were mainly clinical trials involving patients with neurological conditions, particularly poststroke status (n=6) and spinal cord injury (n=2). Several studies provided physiological data, including electroencephalography (n=12), electromyography (n=2), magnetic resonance imaging (n=1), galvanic skin response (n=1), electrodermal activity (n=1), and motor-evoked potential data (n=1). Two studies used noninvasive brain stimulation, and another two used eye tracking. Most studies (n=17) used built-in motion sensors; however, some (n=8) analyzed the data quantitatively. Unity 3D was the most frequently used development platform (n=8). First-person (n=20) and third-person (n=2) perspectives were used, and 4 studies combined both perspectives. Interventions mainly targeted sensorimotor deficits, with improvements in motor and cognitive performance. Sixteen studies addressed body perception, focusing on limb embodiment. Questionnaires were the most frequently used evaluation tools (n=18), and 3 studies used standardized tests. Some studies (n=7) investigated body ownership under visuomotor inconsistencies with or without visuotactile stimulation. XR was primarily applied to enhance sensorimotor recovery and assess device feasibility. Few studies directly measured embodiment (n=4), ownership (n=2), or self-location (n=2). The ability of XR platforms to deliver multisensory feedback appears to facilitate sensorimotor learning and support a more accurate body schema. Conclusions: Evidence from the studies supports the usefulness of XR in enhancing reinforcement learning and facilitating recovery in neurorehabilitation. Tailored XR approaches, which are grounded in embodiment principles and patient-specific needs, show promise for improving outcomes in neurological rehabilitation programs. The AR paradigm, which could offer several advantages, was not explored in depth, perhaps due to its difficult implementation during the period considered.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,015 | 0,014 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».