Smart paddleboard and other assistive veyances
Bibliographic record
Abstract
As a contribution to the new field of WaterHCI (Water-Human-Computer Interfaces), we proposed and developed a smart SUP (Stand-Up Paddleboard) to assist a person with a disability (shoulder + back injury) to continue paddling and cross-country swimming (pulling a paddleboard to carry cargo while swimming). The paddleboard technology consists of two thrusters (motorized propellers) driven (1) in proportion to the flex of a paddle, to maintain the same feeling as normal paddling but with easement on shoulder strain, or (2) in proportion to the tension of a tow line attached to a waist strap around a swimmer’s waist. We also propose a controller for controlling a throttle using paddle flex or the tension of a pull cord. We also propose the use of our throttle control technology in transporting the paddleboard by way of a pulled wagon or an electric cargo bicycle (loaded up with the paddleboard and related supplies), at times when it is necessary to push it up steep hill. The wagon has a pull cord similar to the paddleboard, and the bicycle consists of a handlebar equipped with force sensors to provide the similar effect to pulling the paddleboard or wagon.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.011 | 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".