Guest Editor's Introduction to the Special Section on Virtual Rehabilitation
Bibliographic record
Abstract
This special section of Presence presents papers selected from platform presentations at the 2022 International Society for Virtual Rehabilitation (ISVR) Conference. Now called the World Congress for the International Society for Virtual Rehabilitation (WCISVR), the conference is ISVR's flagship meeting, providing an in-depth presentation of novel technologies and clinical developments in the field of virtual reality and associated topics related to rehabilitation and health improvement. ISVR 2022 was part of the larger RehabWeek 2022 event in Rotterdam, The Netherlands, which attracted 740 attendees from 48 countries. The ISVR presented 30 proceedings papers with 120 co-authors, including 25 podium talks and 2 keynotes. Over 160 delegates from 30 countries attended the sessions.As noted by Denche-Zamorano et al. (2023) in their recent bibliometric analysis, scientific production in the field of virtual and augmented reality research has increased since 2009, and rehabilitation is one of the most prevalent publication domains. The four papers in this special section highlight the diversity of virtual and augmented reality applications across a range of populations, contexts, and settings. Papers explore the potential of virtual environments to provide augmented feedback, enable standardized data collection and implementation of machine learning models to classify and predict performance, and assess and train physical and cognitive abilities in different populations in laboratory-based and community settings.The first two papers in this special section explore the potential of immersive VR to assess and intervene in a context of motor rehabilitation. Wilson Canete, Wright, and Jacobs present a study with healthy young adults, exploring the effects of providing augmented feedback on the estimation of virtual speed in an immersive virtual environment presented in a head-mounted display. Their study contributes to the evidence base to understand how individuals cope with the inherent perception–action differences in VR as compared to the real world, and how to optimally present augmented feedback to optimize sensorimotor inputs. Belger, Poppe, Karnath, Villringer, and Thone-Otto evaluate the use of immersive VR in the assessment of spatial neglect in post-stroke populations. They specifically explore the use of machine learning techniques to identify features in temporal behavioral patterns to detect subtle neglect. The study shows the potential of VR to provide a standardized data collection environment and generate large amounts of precise data suitable for machine learning models.Moving into a cognitive rehabilitation focus, Francova, Jablonska, and Fajnerova outline the potential of assessment and intervention using immersive virtual reality to provide exposure therapy for claustrophobia, presenting a study that designed and evaluated virtual environments for this purpose. The authors evaluate the effectiveness of these virtual environments to induce claustrophobic fear in a small sample of adults with and without claustrophobia.Finally, Gali, Beste Ercan, Atherton, Cross, Heaton, Sayis, and Pares broaden the scope of the technologies and the populations of interest in this section to include a mixed reality environment using a large-scale floor projection that tracks handheld interactive objects to influence interpersonal motor synchronization to a common rhythm in young children. Their study endeavors to move beyond the need for human facilitation of this behavior by evaluating the potential of playful mixed reality environments to induce synchrony among groups of children.In conclusion, these four papers highlight the diversity of VR applications in physical and cognitive rehabilitation and showcase the work being done in this area, providing insights into specific research trends, needs, and future projections in the field of virtual rehabilitation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.032 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".