A Biomedical Engineering Approach to Select an Appropriate Upper Limb Prosthesis
Bibliographic record
Abstract
The loss of a hand or arm is physically and emotionally devastating. The amputees must learn to perform activities of daily living (ADL) and job-related activities with the aid of a prosthesis. Upper limb prostheses are divided into two main control types: body-powered and externally powered. The most common interface for externally-powered prostheses utilizes the electromyogram (EMG) as a signal. This type of device is called myoelectric prosthesis.The fitting process of an upper-limb prosthetic device onto an amputee is not straight forward. The device has to be well-fit, comfortable, and easy-to-use for the ADL and work. Myoelectric prostheses tend to be the first choice of many but they are not suitable for all type of amputations. Therefore, the objective of this study is to assess whether an amputee should be fitted with a body-powered or myoelectric prosthesis.The study started with a survey of the local prosthetists to identify the key factors for fitting upper-limb amputees. The factors will be used to develop criteria for favoring either bodypowered or myoelectric devices. Then, selected engineering measurements of terminal devices such as grip force and slip can be obtained for body-powered and myoelectric prostheses. Then, a prescription process can be developed by using the criteria with the right type of prosthesis to achieve the amputee’s vocational, recreational and psychosocial requirements.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".