Comparative Analysis of Haptic Gloves for Custom-Developed VR Applications
Bibliographic record
Abstract
Abstract This study presents a comprehensive comparative study of three prominent haptic gloves, covering technical specifications such as glove sensitivity in real-world applications. The central hypothesis posits that the superior glove will demonstrate enhanced capabilities in realistic motion and control, consequently leading to broader applicability. The findings are anticipated to offer valuable insights into the relative performance of these gloves and guide their deployment in education, training, and marketing fields. The evaluation methodology comprises a meticulous examination of technical specifications, provided by haptic glove manufacturers and practical implementations, on custom-developed VR environments that include medical, manufacturing, and entertainment industries. The study involves undergraduate students developing custom-constructed VR applications, and collecting and analyzing data for Manus Prime 3, SenseGlove Nova, and bHaptics TactGlove haptic gloves. Randomly selected participants experienced immersive environments using three haptic gloves for comfort, virtual vs actual touch response rates, and ease of use overall. This ongoing study reports the outcomes of comparative analyses using SPSS' One-Way ANOVA, Paired, and Independent Sample t-Tests. Preliminary test results suggest that SenseGlove Nova provides the most significant enhancement and experiences in training professionals. A general objective of this study is to contribute to the expanding field of haptic technology by providing a detailed comparative analysis that informs practitioners and researchers in their pursuit of immersive and interactive experiences.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".