The feedforward control of posture and movement
Bibliographic record
Abstract
Goal-directed arm movements performed in the standing position potentially disturb the body's equilibrium as a result of the multi-linked structure of the musculoskeletal system. To compensate for these disturbances and ensure that stability is maintained, the central nervous system (CNS) organizes postural adjustments preceding and accompanying the voluntary movement in a feedforward manner (Massion 1992) using knowledge of the dynamics of the body (Bouisset and Zattara 1981). To date, most studies investigating the control of posture during voluntary movements in humans have focused on either the role of the postural activity preceding the movement or on the temporal structure of these anticipatory postural adjustments (APAs) with respect to the focal movement. As such, detailed knowledge about the spatial organization of postural activity is lacking. Furthermore, it is not clear how posture is coordinated when the goal of a voluntary movement changes online. Therefore, the studies in this thesis were aimed at addressing these questions to develop a greater understanding of the organization of feedforward postural control during voluntary movements. Muscle activity, kinetics and kinematics were recorded as subjects performed unperturbed and perturbed reaching movements to targets located in multiple directions while standing. Feedforward postural control strategies preceding and accompanying the reaching movements were quantified. Characterization of the spatial and temporal patterns of muscle activity and ground reaction forces of postural adjustments preceding reach movements revealed that muscle activity was directionally-tuned to reach direction and forces that were constrained to two principal directions. Also, muscle synergies were able to explain the spatial and temporal variability in postural muscle activity in the period preceding the reaching movements, suggesting that a modular organization of muscle recruitment is adopted for this task. Overall, these strategies are similar to those observed for feedback postural responses, suggesting that the CNS relies on shared neural structures for controlling posture in both modes of control. Lastly, the nature of postural control was examined when reaching movements were perturbed with a shift of the visual target after the reaching movement was initiated. Here, muscle activity in the legs was consistently modulated prior to changes in the muscle activity related to the online correction of the arm trajectory.Taken together, the findings of this thesis provide important insights into how the brain coordinates the control of posture and movement. This work provides a measure of feedforward postural control strategies in healthy, young adults as a first step to understanding how and why deficits in balance control may occur during the execution of voluntary movements in fall-prone individuals.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".