Finite Element Analysis for Improved Crutches Design
Bibliographic record
Abstract
Crutches are a globally known aid for the walking impaired and have needed improvement.Users who require permanent walking assistance or those who are temporarily injured have voiced complaints about the discomfort endured after consistent use.Not only is satisfaction inadequate with existing designs but the ability to use crutches on different terrains has also been an issue.Crutches are a tool that requires modification in order to better suit the needs of all users.The areas of improvement are sectioned into comfortability for users, versatility in design, and widespread applicability on various terrains.This project intends to modify the common axillary crutch to a more versatile design.When analyzing the existing model, noticeable issues included discomfort and large amounts of stress in the wrists and armpits of users.In addition to aiding the user in motion, the objective of the proposed crutch is to address those issues and fold to serve as a leg rest.This allows the user to elevate their legs at different heights when sitting.Different models of crutches are designed using SolidWorks with general design constraints.A final model is then designed, tested and manufactured.Both the prototype and existing axillary crutch are tested under cyclic loading conditions and a friction coefficient measurement apparatus.The theoretical and measured results are gathered and used to evaluate success for the new design.The strength of the adapters is tested by using the Instron machine, a pressure is applied to the top of the adapter until failure or 2000 lbs.The pressure sensor is designed to slide under the foam padding on the handle of the crutch to allow an accurate measurement of the pressure applied to it.The Arduino pressure system can accurately measure pressure readings.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".