A Portable and Automated Postural Perturbation System for Balance Assessment, Training, and Neuromuscular System Identification
Bibliographic record
Abstract
Abstract To date, a postural perturbation system capable of generating position-, velocity-, and force-controlled perturbations while being portable and suitable for use during various postural scenarios does not exist. Therefore, the purpose of the present study was to design, develop, and test a portable and automated postural perturbation system (PAPPS) that can be used to measure and train postural reactions during sitting, standing, and treadmill walking. The core component of the PAPPS was a linear actuator that provides horizontal perturbations. The actuator could generate arbitrary displacement, velocity, or force perturbations as a function of time. In addition, the PAPPS was able to measure the actuator’s displacement, velocity, and load, which could be used to study postural perturbation responses. The height at which the PAPPS was delivering the perturbations could be easily adjusted to allow for different subject/patient anthropometrics and a wide range of postural scenarios such as sitting, standing, and treadmill walking. The PAPPS generated a peak displacement of 0.6m, a peak velocity of 0.5m∕s, and a peak force of 600N, which is more than sufficient to elicit high intensity postural perturbations. Multiple and nested safety circuits have been implemented into the PAPPS to ensure the safety of the subjects/patients during experiments and/or training. To evaluate the accuracy and repeatability of the PAPPS during position-, velocity-, and force-controlled perturbations, experiments were conducted using sinusoidal, impulse, and ramp profiles as a function of time. Highly sensitive displacement and force sensors that were external to the PAPPS were used to determine the accuracy and repeatability of the proposed device. In addition, a case study was performed to demonstrate the performance of the PAPPS during pseudorandom sinusoidal perturbations that were applied to a healthy individual during sitting. The accuracy and repeatability tests suggest that the PAPPS can generate reliable and high-precision displacement, velocity, and force perturbations. Potential applications of this system include, but are not limited to (1) studies of postural response to various perturbation types and profiles in diverse subject populations during sitting, standing, and treadmill walking, and (2) training of postural balance in diverse patient populations during sitting, standing, and treadmill walking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".