Categorization of compensatory motions in transradial myoelectric prosthesis users
Bibliographic record
Abstract
BACKGROUND: Prosthesis users perform various compensatory motions to accommodate for the loss of the hand and wrist as well as the reduced functionality of a prosthetic hand. OBJECTIVES: Investigate different compensation strategies that are performed by prosthesis users. STUDY DESIGN: Comparative analysis. METHODS: A total of 20 able-bodied subjects and 4 prosthesis users performed a set of bimanual activities. Movements of the trunk and head were recorded using a motion capture system and a digital video recorder. Clinical motion angles were calculated to assess the compensatory motions made by the prosthesis users. The video recording also assisted in visually identifying the compensations. RESULTS: Compensatory motions by the prosthesis users were evident in the tasks performed (slicing and stirring activities) as compared to the benchmark of able-bodied subjects. Compensations took the form of a measured increase in range of motion, an observed adoption of a new posture during task execution, and prepositioning of items in the workspace prior to initiating a given task. CONCLUSION: Compensatory motions were performed by prosthesis users during the selected tasks. These can be categorized into three different types of compensations. Clinical relevance Proper identification and classification of compensatory motions performed by prosthesis users into three distinct forms allows clinicians and researchers to accurately identify and quantify movement. It will assist in evaluating new prosthetic interventions by providing distinct terminology that is easily understood and can be shared between research institutions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".