Proposed design of a Motorized Force Feedback haptics Device with Rotary Joint Module and spherical friction clutch
Bibliographic record
Abstract
Haptic technology is a concept-based technology, which helps the user to interface with a device via the sense of touch. The user applies force, create vibrations and give motion with the help of the haptic device. So haptic devices can be put into the category of input devices that works on the principle of force feedback. The main applications where an educational haptic device can be used are the areas where there is a hands-on nature experiments are there. With the help of force feedback devices, it is possible to get the attention of learners in a more compelling way to get involved into the learning process. It also helps to build the underscores connection between science, technology, engineering, mathematical theory and physical reality. Haptic devices, which are used to implement force feedback, are programmed to generate physical interactions that help the learners to improve practical knowledge about their scientific and mathematical areas. Haptic devices can also help to generate the requirement, which can be further extended for interdisciplinary robotics education. IT can be a source of inspiration for very young students to explore these areas at length. In this research paper, we have proposed a design for a haptic device, which is based on motorized mechanism and has a friction clutch. This haptic device can be used to make the learning process better and convenient.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".