Bibliographic record
Abstract
This project aimed to develop a self tightening and loosening ankle brace, to provide support only when needed. Ankle braces are widely used, from healing athlete’s injuries, to aiding elderly with everyday walking. Therefore, this device must be effective and comfortable in a variety of situations. Previous research discusses the benefits of allowing ankle injuries to heal under their own body weight, and the importance of strengthening the ankle without support. Other studies have proved that ankle braces are an important component in reducing the risk of injury and reinjury. Today’s solutions are predominantly static which only address one of the issues. Past studies have investigated the various styles of ankle braces and found that semi-rigid designs tend to be the most comfortable for the average person. The goal of this research was to achieve all the top qualities found in previous studies. This led to the idea of a semi-rigid brace that would allow the ankle to be free of support or well supported based on need. The iterative design process was followed for each component then integrated into the system. Testing was completed after each new component was added to ensure a cohesive design was achieved. The final design consisted of a soft ankle wrap with two rigid 3D printed plates attached on either side of the ankle. The plates are sinched together by a ratcheting mechanism located on the hip. This mechanism is powered by a 12V DC motor and a Lithium-Ion battery pack. The system is actuated by an inertial measurement unit (IMU) when inversion or eversion is detected. This work proves feasibility for a self tightening and loosening ankle brace. Future testing should be done on the device in the fully tightened and loosen states.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".