Myoelectric prosthesis users and non-disabled individuals wearing a simulated prosthesis exhibit similar compensatory movement strategies
Bibliographic record
Abstract
BACKGROUND: Research studies on upper limb prosthesis function often rely on the use of simulated myoelectric prostheses (attached to and operated by individuals with intact limbs), primarily to increase participant sample size. However, it is not known if these devices elicit the same movement strategies as myoelectric prostheses (operated by individuals with amputation). The objective of this study was to address the question of whether non-disabled individuals using simulated prostheses employ the same compensatory movements (measured by hand and upper body kinematics) as individuals who use actual myoelectric prostheses. METHODS: The upper limb movements of two participant groups were investigated: (1) twelve non-disabled individuals wearing a simulated prosthesis, and (2) three individuals with transradial amputation using their custom-fitted myoelectric devices. Motion capture was used for data collection while participants performed a standardized functional task. Performance metrics, hand movements, and upper body angular kinematics were calculated. For each participant group, these measures were compared to those from a normative baseline dataset. Each deviation from normative movement behaviour, by either participant group, indicated that compensatory movements were used during task performance. RESULTS: Results show that participants using either a simulated or actual myoelectric prosthesis exhibited similar deviations from normative behaviour in phase durations, hand velocities, hand trajectories, number of movement units, grip aperture plateaus, and trunk and shoulder ranges of motion. CONCLUSIONS: This study suggests that the use of a simulated prosthetic device in upper limb research offers a reasonable approximation of compensatory movements employed by a low- to moderately-skilled transradial myoelectric prosthesis user.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".