EMG-biofeedback and load sharing problem in assistive and rehabilitation orthotic devices
Bibliographic record
Abstract
Biofeedback signals have been frequently used for rehabilitation purposes, and in design and calibration of orthotic and prosthetic devices. Whenever one or a couple of muscles of a joint are chosen for rehabilitation or control of a device, it's assumed that a specific load sharing or activation pattern exists among them for each individual and for each specific joint demand. Indeterminacy or a load sharing problem arises from having more muscles crossing a joint than needed to perform all possible movements. It's proven that muscle activation patterns depend on fatigue, the task (isometric/isokinetic, concentric/eccentric), load type, mental demands, etc. The most used biofeedbacks are electromyogram of one of the muscles or the joint torque signal. An important question is if they can be used interchangeably. This study investigated if the choice of biofeedback can also change the activation pattern in the two main elbow flexors. The results of this experiment on six healthy subjects and seven activation levels, indicated that change in biofeedback type had a significant effect on the activation ratio of these two muscles.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".