Comparison of musculoskeletal robot biomechanical properties to human participants using motion study
Bibliographic record
Abstract
Abstract Advanced robotic systems that replicate musculoskeletal structure and function have significant potential for a wide range of applications. Although they are proposed to be better platforms for biomedical applications, little is known about how well current musculoskeletal humanoid systems mimic the motion and force profiles of humans. This is particularly relevant to the field of tendon tissue engineering, where engineered grafts require advanced bioreactor systems that accurately replicate the kinetic and kinematic profiles experienced by the humans in vivo . A motion study was conducted comparing the kinetic and kinematic profiles produced by a musculoskeletal humanoid robot shoulder to a group of human participants completing abduction/adduction tasks. Results from the study indicate that the humanoid arm can be programed to either replicate the kinematic profile or the kinetic profile of human participants during task completion, but not both simultaneously. This study supports the use of humanoid robots for applications such as tissue engineering and highlights suggestions to further enhance the physiologic relevance of musculoskeletal humanoid robotic platforms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".