A New Modeling Method to Characterize the Stance Control Function of Prosthetic Knee Joints
Bibliographic record
Abstract
OBJECTIVE: Biomechanical models can inform design and optimization of prosthetic devices by connecting empirically derived biomechanical data to device design parameters. A new method is presented to characterize the function of prosthetic stance control under mobility conditions associated with activities of daily living. The method is based on a model of the gait modes corresponding to finite stance control states. METHODS: Empirical data from amputee and simulated gait were acquired using a custom-built wearable instrument and input into the model. RESULTS: The modeling approach was shown to be robust, responsive, and capable of accurate characterization of controller function under diverse locomotor and prosthetic setup conditions. CONCLUSION: Future work is focused on the development of a fully self-contained wearable system, to facilitate collection of large datasets across a variety of user demographics, controller designs, and activities of daily living. SIGNIFICANCE: The method offers predictive capability, which can assist in the virtual testing of new designs or modifications.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".