Knee joint mechanism that mimics elastic characteristics and bending in human running
Bibliographic record
Abstract
Analysis of human running has revealed that the motion of the human leg can be modeled by a compression spring because the leg's joints behave like a torsion spring in the stance phase. Moreover, the knee bends rapidly to avoid contact of the foot with the ground in the swing phase. In this paper, we describe the development of a knee joint mechanism that mimics the elastic characteristics of the stance leg and rapid bending knee of the idling leg of a running human. The knee was equipped with a mechanism comprising two leaf springs and a worm gear for adjusting the joint stiffness and high-speed bending knee. Using this mechanism, we were able to achieve joint stiffness within the range of human knee joints that could be adjusted by varying the effective length of one of the leaf springs. In addition, the mechanism was able to bend rapidly by changing the angle between the two leaf springs. The equation proposed for calculating the joint stiffness considers the difference between the position of the fixed point of the leaf spring and the position of the rotational center of the joint. We evaluated the performance of the adjustable joint stiffness and the effectiveness of the proposed equation for joint stiffness and high-speed knee bending. We were able to make a bipedal robot hop using pelvic oscillation for storing energy produced by the resonance to leg elasticity and confirmed the mechanism could produce large torque 210 Nm.
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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 | 0.000 | 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".