Design and Evaluation of Wearable Haptics Device for Weight Perception in Vocational Trade
Bibliographic record
Abstract
Wearable devices have a good role in haptics and Virtual Reality (VR) technologies. Weight perception during the VR interactions would give a more realistic immersive experience to the user and that would give psycho-motor skill training. User performance is another aspect that would add to the advantages of wearable haptic devices. Scaffolding work is a very complex assembling task in the construction industry and proper training is insufficient in this area. The technology-based training would be helpful for reducing accidents and improving the outcome. So VR-based solutions along with wearable haptics would be the best option for this. This paper described the design and prototype development of a wearable haptic device based on the systematic approach that can be used for scaffolding work. This paper also reported the user experiments conducted with the participants and presented their experiences. These studies revealed the details of the weight perception and comfort level of the participants when experiencing the device.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".