Exploiting invariant structure for controlling multiple muscles in anthropomorphic legs: II. Experimental evidence for three equilibrium-point-based synergies during human pedaling
Bibliographic record
Abstract
Developing a musculoskeletal robot with multiple muscles is important for not only establishing a novel robot that achieves coordination with a human but also in understanding the framework of motor control in the human body. Our pioneering work proposed a biologically inspired control framework for multiple redundant muscles; however, very few practical results have been reported on controlling a musculoskeletal robot. This study focused on confirming the usability of the proposed biologically inspired control framework, which is referred to as equilibrium-point (EP)-based synergyies and is expressed by the activation balance of agonist-antagonist muscle pairs. Electromyography data obtained from the pedaling task of five subjects were analyzed based on the concept of the EP-based synergies, and were then used to control the musculoskeletal lower limb robot. Three EP-based synergies obtained from all the five subjects indicated that the musculoskeletal robot achieved forward pedaling. This indicates the utility of the EP-based synergies as well as the modularity of motor control in humans.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".