Use of Induced Acceleration to Quantify the (De)stabilization Effect of External and Internal Forces on Postural Responses
Bibliographic record
Abstract
Due to the mechanical coupling between the body segments, it is impossible to see with the naked eye the causes of body movements and understand the interaction between movements of different body parts. The goal of this paper is to investigate the use of induced acceleration analysis to reveal the causes of body movements. We derive the analytical equations to calculate induced accelerations and evaluate its potential to study human postural responses to support-surface translations. We measured the kinematic and kinetic responses of a subject to sudden forward and backward translations of a moving platform. The kinematic and kinetics served as input to the induced acceleration analyses. The induced accelerations showed explicitly that the platform acceleration and deceleration contributed to the destabilization and restabilization of standing balance, respectively. Furthermore, the joint torques, coriolis and centrifugal forces caused by swinging of the arms, contributed positively to stabilization of the Center of Mass. It is concluded that induced acceleration analyses is a valuable tool in understanding balance responses to different kinds of perturbations and may help to identify the causes of movement in different pathologies.
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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 | 0.001 | 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".