Dynamic control of a moving platform using the CAREN system to optimize walking invirtual reality environments
Bibliographic record
Abstract
Virtual reality (VR) technology offers the opportunity to expose patients to complex physical environments without physical danger and thus provides a wide range of opportunities for locomotor training or the study of human postural and walking behavior. A VR-based locomotor training system has been developed for gait rehabilitation post-stroke. A clinical study has shown that persons after stroke are able to adapt and benefit from this novel system wherein they walk into virtual environments (VEs) on a self-paced treadmill mounted on a platform with 6 degrees of freedom. This platform is programmed to mimic changes in the terrain encountered in the VEs. While engaging in these VEs, excessive trunk movements and speed alterations have been observed, especially during the pitch perturbations accompanying uphill or downhill terrain changes. An in-depth study of the subject's behavior in relation to the platform movements revealed that the platform rotational axes need to be modified, as previously shown by Barton et al, and in addition did not consider the subject's position on the treadmill. The aim of this study was to determine an optimal solution to simulate walking in real life when engaging in VEs.
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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 | 0.001 | 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 | 0.001 | 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".