Task-dependent modulation of joint stiffness
Bibliographic record
Abstract
Joint stiffness defines the dynamic relationship between the position of the joint and the torque acting about it; hence it is important in the control of movements and posture. Joint stiffness consists of two components: intrinsic stiffness, which is due to the viscoelastic properties of the joint, muscle, and connective tissue and the inertia of the limb; reflex stiffness, which arises due to the torque produced by the stretch reflex response. During some tasks, visual, vestibular and somatosensory mechanisms may also contribute to the estimated joint stiffness. This thesis explores how people modulate their joint stiffness to perform different tasks. Numerous studies have investigated whether subjects can modulate their reflex stiffness voluntarily but have produced contradictory results; a possible reason for this uncertainty is the lack of a proper feedback to provide to subjects. This thesis presents a novel algorithm that can estimate intrinsic and reflex stiffness in real-time. Experimental results are then presented that show that using the estimates generated by the real-time algorithm as feedback, subjects could control reflex stiffness independently of intrinsic stiffness. In another set of experiments, subjects were given a task that would be performed optimally by modulating their joint stiffness. However, subjects produced voluntary torques based on visual feedback, rather than modulating their joint stiffness. These voluntary torques were found to be correlated to the velocity of the visual feedback. Thus, though mechanisms exist for subjects to modulate their intrinsic and reflex stiffness independently and voluntarily, subjects did not do so. Rather they preferred to use visual feedback to generate the voluntary torques needed to perform the task. Based on the findings of this thesis we conclude that subjects can modulate their joint stiffness in numerous ways—by altering intrinsic stiffness, reflex stiffness or voluntary components. However, i
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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 | 0.002 |
| 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 | 0.000 | 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".