Multidirectional Human-in-the-Loop Balance Robotic System
Bibliographic record
Abstract
We present the design and performance of a novel multidirectional balance robotic system. This robot adds mediolateral standing balance control to the functionality of previous systems. To evaluate the performance of the system, we quantified its motion-tracking capability by applying target trajectories related to perturbed and unperturbed quiet standing balance. We observed minimal delays (9.4–13.9 ms) and near-unity gain up to 4 Hz when tracking multi-sine trajectories and small errors when tracking natural balance trajectories (≤0.05 mm, corresponding to ≤0.009$^\circ $), which were all below human perceptual thresholds reported for standing balance (150 ms and 0.17$^\circ $at 0.06$^\circ $/s). Next, we evaluated the human-in-the-loop real-time robot performance when participants (N = 6) maintained their upright balance in the mediolateral direction while firmly secured to the robot. The results revealed small errors between the predicted and robotic motion ($^\circ $) as well as between the robotic and measured human motion ($^\circ $) in real-time applications. The sub-15ms delays, combined with submillimeter movement errors and relative robot-human movements, enable realistic multidirectional simulations of human balance. These unique robotic features open up new research opportunities for exploring the sensorimotor principles and biomechanical interactions underlying the multidirectional control of balance, and may ultimately be used to assess and rehabilitate standing balance deficits in older adults and clinical populations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".