Intro to haptic communications for high school students
Bibliographic record
Abstract
Most haptic educational papers focus on using haptic devices as an aid to teaching. We introduced the use of vibro-tactile communication as well as tactile and kinesthetic sensing and their combination to high school students in order to introduce them to the field of haptics and its applications. The effectiveness of our approach for introducing the concepts as well as the technical issues associated with haptics to this age group was relatively successful. Initially, various physiological response properties of the human tactile system, including the kinesthetic component as well as the tactual (force, texture) properties were presented. The students were then exposed to theory, hardware (sensors, actuators) interfacing as well as the software to close the loop between sensing and haptic feedback after initially investigating open loop configurations. Vibro-tactile (i.e., pager motors) were used in the labs as chiefly communication devices to convey either pan-tilt from an accelerometer (kinesthetic) or the intensity of force exerted on a force pressure sensor. The students enjoyed the experience, especially the hands-on nature of the project and their only major complaint was that there was too much programming. Our future intent is to abstract the building blocks, simplify the software interfacing so as to allow students of various levels and capabilities to effectively be creative and imaginative in developing various haptic communication applications and tools
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.120 | 0.060 |
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".