Quantifying Multisensory Immersive Experiences using Wearables: Is (Stimulating) More (Senses) Always Merrier?
Bibliographic record
Abstract
Virtual reality applications are on the rise and touching numerous domains, including healthcare, training, and gaming, to name a few. Existing experiences, however, are not fully immersive, as only two senses (audio-visual) are stimulated. To overcome this limitation, olfactory and haptic devices are emerging, thus making multisensory immersive experiences a reality. To date, however, little is known about the impact that each stimulated sense has on the overall experience, as well as on the user’s sense of e.g., realism, immersion, and engagement. In this pilot, we aim to answer this question. Using a multisensory pod, sixteen participants were immersed in a 2.5-minute virtual world, where smells, vibroacoustic, and somatosensory stimuli (i.e., wind and heat) were presented, in addition to 360-degree video and surround sound. Using two wearable devices, we kept track of the user’s heart rate, breathing rate, skin temperature, blood volume pulse, and electrodermal activity while they were immersed. In this paper, we report the impact that stimulating different senses had on the users’ overall experience, sense of presence, immersion, realism, flow, cybersickness, and emotional states, both subjectively, as well as objectively using features extracted from the wearable devices.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".