Design and Rapid Construction of a Cost-Effective Virtual Haptic Device
Bibliographic record
Abstract
Over the past few decades, the rapid development of haptic technology has made it widely used in the fields of virtual reality, video games, rehabilitation, education, and manufacturing. This article presents a study on the rapid construction of a cost-effective virtual haptic device. In this connection, this article proposed a new design concept called the haptic environment. A whole haptic system is divided into an environment, which consists of all common modules, and a haptic robot. As such, a customized haptic robot can be plugged into the environment to form a haptic system. The environment also contains the adapters that facilitate the accommodation of the customized haptic robot into the environment. To show the effectiveness of this concept, a three-degree-of-freedom robot along with the environment was implemented in this article. Experiments were conducted to understand the performance of the prototype of the haptic system, as developed, and the results show that the developed haptic system has the graphical refresh frequency of about 60 Hz and the haptic rendering frequency of about 500 Hz, nearly three times faster than the Phantom Omni device commercially available. The contribution of the study reported in this article includes the demonstration of the feasibility of the rapid construction of a virtual haptic device and the provision of a cost-effective haptic device for applications in the robotic rehabilitation and human assistive systems.
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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 | 0.000 |
| 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) | 0.003 | 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".