Bibliographic record
Abstract
Research performed in the area of haptics has produced some remarkable results with a variety of haptic devices. However, haptic-based applications are designed to consider only a particular haptic device. Therefore, the functionality of a haptic-based system is limited by the chosen device's features, such as workspace, device's inertia, friction, number of points of interaction, number of degrees of freedom, and maximum force that can be exerted. In short, these haptic-based systems are limited for use with a certain haptic device. Without a doubt, the need for a software tool to adapt existing haptic-based systems to the capabilities of another haptic device is evident. On the other hand, one of the main advantages of using haptic devices is the possibility of saving data during the haptic interaction. Our proposed framework, which is called Adaptive Haptic Application (AdHapticA), deals with both issues: It automatically adapts a haptic-based system to be used with another haptic device and saves the corresponding haptic data for quantitative evaluation. The AdHapticA framework can be used to study the feasibility of certain haptic devices to meet the requirements of an application. A case study is presented to evaluate single-point interaction and hand exoskeleton haptic devices for authentication purposes by using the same virtual scenario. The applicability of the proposed framework is shown, and the results obtained from the haptic data are captured when a particular application is performed with either single-point (desktop device) or multipoint interaction devices (hand exoskeleton). Therefore, the results have shown that current hand exoskeleton devices are less suitable for tasks that require a certain level of precision, like haptic-biometric-based tasks.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".