Comparison of trunk muscle forces, spinal loads and stability estimated by one stability- and three EMG-assisted optimization approaches
Bibliographic record
Abstract
Various hybrid EMG-assisted optimization (EMGAO) approaches are commonly used to estimate muscle forces and joint loads of human musculoskeletal systems. Use of EMG data and optimization enables the EMGAO models to account for inter- and intra-individual variations in muscle recruitments while satisfying equilibrium requirements. Due to implications in ergonomics/prevention and rehabilitation/treatment managements of low-back disorders, there is a need to evaluate existing approaches. The present study aimed to compare predictions of three different EMGAO and one stability-based optimization (OPT) approaches for trunk muscle forces, spinal loads, and stability. Identical measured kinematics/EMG data and anatomical model were used in all approaches when simulating several sagittally symmetric static activities. Results indicated substantial inter-model differences in predicted muscle forces (up to 123% and 90% for total muscle forces in tasks with upright and flexed postures, respectively) and spinal loads (up to 74% and 78% for compression loads in upright and flexed postures, respectively). Results of EMGAO models markedly varied depending on the manner in which correction (gain) factors were introduced. Large range of gain values (from ∼0.47 to 41) was estimated in each model. While EMGAO methods predicted an unstable spine for some tasks, OPT predicted, as intended, either a meta-stable or stable states in all simulated tasks. An unrealistic unstable state of the spine predicted by EMGAO methods for some of the simulated tasks (which are in reality stable) could be an indication of the shortcoming of these models in proper prediction of muscle forces.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".