The Effects of a Virtual Environment and Robot-Generated Haptic Forces on the Coordination of the Lower Limb During Gait in Chronic Stroke Using Planar and 3D Phase Diagrams
Bibliographic record
Abstract
Previous studies have combined robot-controlled haptic tensile forces with virtual reality (VR) to produce gait adaptation and post-adaptation effects in spatiotemporal gait and postural outcomes in healthy young, elderly, and chronic stroke individuals. The present study focuses on kinematic and dynamic adaptation and post-adaptation effects of lower limb segment coordination, during and after a 15 N tensile force exposure by presenting two representations of 3D coordination - planar and phase diagrams. One chronic stroke subject (73 y.o., 8 months post-stroke, RH) and one age-matched control subject (71 y.o, RH) walked on a self-paced treadmill in a virtual environment holding a robot-controlled haptic leash. The paradigm consisted of a 30 s pre-force baseline epoch, followed by a 60 s tensile force and a 60 s post-force epoch. Both the chronic stroke and control subject showed evidence of changes in bilateral intersegmental coordination of the lower limb to accompany gait speed increases during force and post-force epochs. In particular, both subjects increased dorsiflexion of the non-dominant leg during and after the 15 N force exposure. Changes in limb segment coordination also corresponded to bilateral increases in 3D intersegmental trajectory areas. While there was no evidence of increased symmetry based on left and right leg plane comparisons, slight increases in angular velocity were noted just prior to and during the swing phase of the paretic leg during force and post-force epochs. These findings were further substantiated by Sobolev norms which increased bilaterally and proportionally for force and post-force epochs. Adaptation and post-adaptation effects seen in bilateral lower limb coordination when haptic forces were present and released suggest proportional increases in the kinematic and dynamic outcomes. Further investigation involving a wider range of chronic stroke functional levels should be conducted.
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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 | 0.001 |
| 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) | 0.001 | 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".