Toward Physiological Detection of a “Just-Right” Challenge Level for Motor Learning in Immersive Virtual Reality: Protocol for a Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Motor learning, a primary goal of pediatric rehabilitation, is facilitated when tasks are presented at a "just-right" challenge level-at the edge of the child's current abilities, yet attainable enough to motivate the child in persistent efforts for success. Immersive virtual reality (VR) may be ideally suited for "just-right" task challenges because it enables precise adjustments of task parameters in motivating environments. Rehabilitation-specific VR tasks often use dynamic difficulty algorithms based on task performance to personalize task difficulty. However, these approaches do not consider relevant cognitive processes that could also impact "just-right" challenges, such as attention and engagement. Objective physiological measurement of these cognitive processes using wearable sensors could support their integration within "just-right" challenge detection and prediction algorithms. As a first step, it is important to explore relationships between objectively and subjectively measured psychophysiological states at progressively challenging task difficulty levels. OBJECTIVE: This study aims to (1) evaluate the performance of wearable sensors in a novel movement-based motor learning immersive VR task; (2) evaluate changes in physiological data at 3 task difficulty levels; and (3) explore the relationship between physiological data, task performance, and self-reported cognitive processes at each task difficulty level. METHODS: This study uses the within-participant experimental design. Typically developing children and youth aged 8-16 years will be recruited to take part in a single 90-minute data collection session. Physiological sensors include electrodermal activity, heart rate, electroencephalography, and eye-tracking. After collecting physiological data at rest, participants will play a seated unimanual immersive VR task involving bouncing a virtual ball on a virtual racket. They will first play for 3 minutes at a predefined medium level of difficulty to determine their baseline ability level and then at a personalized choice of 3 progressive difficulty levels of 3 minutes each. Following each 3-minute session, participants will complete a short Likert-scale questionnaire evaluating engagement, attention, cognitive workload, physical effort, self-efficacy, and motivation. Data loss and data quality will be calculated for each sensor. Repeated-measures ANOVAs will evaluate changes in physiological response at each difficulty level. Correlation analyses will determine individual relationships between task performance, physiological data, and self-reported data at each difficulty level. RESULTS: Research ethics board approval has been obtained, and data collection is underway. Data collection was conducted on December 12, 2023, and April 12, 2024, with a total of 15 typically developing children. Data analysis has been completed, and results are expected to be published in the fall of 2024. CONCLUSIONS: Wearable sensors may provide insights into the physiological effects of immersive VR task interaction at progressive difficulty levels in children and youth. Understanding the relationship between physiological and self-reported cognitive processes is a first step in better identifying and predicting "just-right" task challenges during immersive VR motor learning interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55730.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".