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Record W4412399855 · doi:10.2196/63874

A Virtual Reality Force Control Training System on Brain Activation: Functional Near-Infrared Spectroscopy (fNIRS) Study

2025· article· en· W4412399855 on OpenAlexvenueno aff
Luigi Gan, Chien-Ju Lin, Hsiao-Feng Chieh, Kai‐Nan An, Fong‐Chin Su

Bibliographic record

VenueJMIR Serious Games · 2025
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsFunctional near-infrared spectroscopyPhysical medicine and rehabilitationRehabilitationVirtual realityPremotor cortexPsychologyPrefrontal cortexNeuroplasticityBrain activity and meditationTask (project management)Activities of daily livingComputer scienceMedicineNeuroscienceHuman–computer interactionCognitionElectroencephalography

Abstract

fetched live from OpenAlex

Background: Aging can bring upon several effects that can hinder one's quality of life. One of the effects is the decline in one's ability to perform activities of daily living, which is caused by the loss of hand function due to aging. To mitigate this, several virtual reality (VR)-based training or rehabilitation systems that use hand tracking were developed. Although these systems are effective, immersive, and can promote motivation, they are mostly limited to providing range of motion exercises. The addition of a force control component to the hand tracking of these systems could make them even more effective at improving or restoring hand function, as the majority of activities of daily living require a degree of force control. Objective: This study aimed to compare the effects of 2 VR input systems: regular hand tracking and the novel VR input system in this study, which incorporate force control to regular hand tracking on the brain activity of younger and older adults. The degree of cortical activity during a training or rehabilitation task is linked to better functional outcomes and improvements of neuroplasticity. Methods: Twelve younger adults (mean age 25.00, SD 4.50 years) and 12 older adults (mean age 73.00, SD 3.6 years) were recruited to play a game specifically developed for this study using 2 VR input systems. Brain activity during gameplay was recorded using functional near-infrared spectroscopy over the following cortical regions: prefrontal cortex (PFC), premotor cortex (PMC), supplementary motor area (SMA), and primary motor cortex (M1). Results: Compared with the regular hand-tracking system, adding a force control component increased average oxygenated hemoglobin (HbO) concentrations and decreased deoxygenated hemoglobin (HbR) concentrations in key brain regions. In young adults, these changes were observed in the right PMC and right M1. In older adults, higher HbO and lower HbR concentrations appeared in the right PFC, bilateral M1, and right SMA (HbR only). The force control component also led to more widespread activity across all ROIs. Conclusions: The novel input system in this study can be used for improving or restoring hand function. The results of this study can be used as a reference for the development of better VR-based training or rehabilitation systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.304
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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