Adjustable Liners and Sockets for Prosthetic Devices
Bibliographic record
Abstract
INTRODUCTION Ras Labs’ Synthetic Muscle™ will allow amputees to continue their active lives without needing to adjust the fitting of their prosthetic device(s) throughout the day. This technology promises to resolve major issues facing amputees, most notably the pain of prosthetic slippage and tissue breakdown. Synthetic Muscle™, comprising electroactive polymers (EAPs), actively expand or contract at low voltages, while offering impact resistance and pressure sensing, all in one integrated solution. The main objectives of this project is to determine the feasibility of the EAP pads incorporated into prosthetic liners or sockets and to create prototypes of these EAP based shape-morphing pad systems. In collaboration with UPI, testing of these EAP based pads located in strategic areas of the socket was initiated with customers (BK and AK) for evaluation and feedback. Characterization of Synthetic Muscle™ as dual use pressure sensors was initiated. This is a continuation Ras Labs’ dynamic prosthetic pad project, which demonstrated how the volume of the EAP can be changed from applying a low voltage and operating temperatures for use in adjustable prosthetic liners and sockets 1-8. Abstract PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/32048/24462 How to cite: Rasmussen L, Rodriguez S, Bowers M, Smith D, Martino G, Rizzo L, Scheiber C, d’Almeida J, Dillis C. Adjustable Liners and Sockets for Prosthetic Devices. CANADIAN PROSTHETICS & ORTHOTICS JOURNAL, VOLUME 1, ISSUE 2, 2018; ABSTRACT, POSTER PRESENTATION AT THE AOPA’S 101ST NATIONAL ASSEMBLY, SEPT. 26-29, VANCOUVER, CANADA, 2018. DOI: https://doi.org/10.33137/cpoj.v1i2.32048 Abstracts were Peer-reviewed by the American Orthotic Prosthetic Association (AOPA) 101st National Assembly Scientific Committee. http://www.aopanet.org/
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.013 |
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".