Multi-Joint Coordination of Vertical Arm Movement
Bibliographic record
Abstract
A model of the human arm was developed to study coordination of multi-joint movement in the vertical plane. The arm was represented as a two-segment, two-degree of freedom dynamic system with net muscle torques acting at the shoulder and elbow. Kinematic data were collected from a subject who performed unrestrained vertical movements with only the initial and final hand elevations prescribed. Movements were performed with and without a hand-held load. The method of computed torques was implemented to obtain net muscle torques, which enables position and velocity feedback to be used to estimate joint angular accelerations that produce a more stable simulation of arm movement. The model simulation was then used to calculate the contributions of the net muscle torques, gravitational torques and velocity-interaction torques to the angular accelerations of the shoulder and elbow and also to the vertical acceleration of the hand. The net muscle torques and gravity were the prime movers of the arm. The velocity-dependent effects contributed little to the dynamics of arm movement and were, in fact, insignificant when the hand was loaded. The muscles of the shoulder and elbow acted synergistically to elevate the arm in the sagittal plane. The hand was accelerated upward by the elbow first, until the point of maximum elbow flexion, after which the shoulder became the prime mover. Gravity acted consistently to accelerate the hand downward. Coordination was notably invariant to changes in external load. Some compensation for load was observed in the control, and these differences were attributed mainly to an increase in system inertia.
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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".