Move, React, Repeat! The Role of Continuous Cues in Immersive Exergames
Bibliographic record
Abstract
Immersive virtual reality (VR) exergames blend playful environments with physical activities. In such games, providing continuous cues, i.e., real-time indications of how the movements are being performed, can guide players and improve the execution of movements. However, research about how continuous cues impact player performance and experience in VR exergames remains sparse. To this end, we conducted a within-participants study (N=32) with four conditions: (i) no, (ii) audio, (iii) visual, and (iv) audiovisual cueing. The results show that both unimodal and bimodal cueing improve the player experience without causing cybersickness, while bimodal cueing improves pose accuracy. We provide three main design implications for VR exergames: (i) considering the granularity of cues, (ii) exploring different continuous cue designs, and (iii) underlining the use of continuous cues in other immersive experiences.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".