Muscle Tension Estimation in the Presence of Neuromuscular Impairment
Bibliographic record
Abstract
Static optimization approaches to estimating muscle tensions rely on the assumption that the muscle activity pattern is in some sense optimal. However, in the case of individuals with a neuromuscular impairment, this assumption is likely not to hold true. We present an approach to muscle tension estimation that does not rely on any optimality assumptions. First, the nature of the impairment is estimated by reformulating the relationship between the muscle tensions and the external forces produced in terms of the deviation from the expected activation in the unimpaired case. This formulation allows the information from several force production tasks to be treated as a single coupled system. In a second step, the identified impairments are used to obtain a novel cost function for the muscle tension estimation task. In a simulation study of the index finger, the proposed method resulted in muscle tension errors with a mean norm of 23.3 ± 26.8% (percentage of the true solution norm), compared to 52.6 ± 24.8% when solving the estimation task using a cost function consisting of the sum of squared muscle stresses. Performance was also examined as a function of the amount of error in the kinematic and muscle Jacobians and found to remain superior to the performance of the squared muscle stress cost function throughout the range examined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".