MyoBack: A Musculoskeletal Model of the Human Back with Integrated Exoskeleton
Bibliographic record
Abstract
Abstract Given the challenges of real-life experimentation, musculoskeletal simulation models could become essential in biomedical research. This is especially critical for the human back, a key structure involved in daily movements, where modeling and simulation could streamline design and support the development of treatments and robotic rehabilitation techniques, such as exoskeletons. However, musculoskeletal simulation engines are computationally demanding and lack contact dynamics, restricting current models’ use in studying prolonged behaviors or optimizing system design while maintaining physiological accuracy. To overcome this limitation, this work proposes MyoBack, a human back model part of the MyoSuite framework relying on the physics engine MuJoCo. This model is derived from a physiologically accurate model built in the state-of-the-art musculoskeletal simulation software OpenSim and replicates the latter’s kinematic properties accurately, with some discrepancies regarding muscle dynamics stemming from engine differences. The MyoBack model was also validated empirically by integrating a passive back exoskeleton in simulation and comparing forces exerted on the back with values from experimental trials. Over different tasks, the model reproduced measured force progressions well, resulting in RMSE = 11% for a stoop and RMSE = 16% for a squat motion pattern relative to peak forces. The MyoBack model can be accessed here: https://github.com/rohwalia/MyoBack
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".