Walking with robot-generated haptic forces in a virtual environment: a new approach to analyze lower limb coordination
Bibliographic record
Abstract
BACKGROUND: Walking with a haptic tensile force applied to the hand in a virtual environment (VE) can induce adaptation effects in both chronic stroke and non-stroke individuals. These effects are reflected in spatiotemporal outcomes such as gait speed. However, the concurrent kinematic changes occurring in bilateral lower limb coordination have yet to be explored. METHODS: Chronic stroke participants were stratified based on overground gait speed into lower functioning (LF < 0.8 m/s, N = 7) and higher functioning (HF ≥ 0.8 m/s, N = 7) subgroups. These subgroups and an age-matched control group (N = 14, CG) walked on a self-paced treadmill in a VE with either robot-generated haptic leash forces delivered to the hand and then released or with an instrumented cane. Walking in both leash (10 and 15 N) and cane conditions were compared to pre-force baseline values to evaluate changes in lower limb coordination outcomes. RESULTS: All groups showed some kinematic changes in thigh, leg and foot segments when gait speed increased during force and post-force leash as well as cane walking. These changes were also reflected in intersegmental coordination and 3D phase diagrams, which illustrated increased intersegmental trajectory areas (p < 0.05) and angular velocity. These increases could also be observed when the paretic leg transitions from stance to swing phases while walking with the haptic leash. The Sobolev norm values accounted for both angular position and angular velocity, providing a single value for potentially quantifying bilateral (i.e. non-paretic vs paretic) coordination during walking. These values tended to increase (p < 0.05) proportionally for both limbs during force and post-force epochs as gait speed tended to increase. CONCLUSIONS: Individuals with chronic stroke who increased their gait speed when walking with tensile haptic forces and immediately after force removal, also displayed moderate concurrent changes in lower limb intersegmental coordination patterns in terms of angular displacement and velocity. Similar results were also seen with cane walking. Although symmetry was less affected, these findings appear favourable to the functional recovery of gait. Both the use of 3D phase diagrams and assigning Sobolev norm values are potentially effective for detecting and quantifying these coordination changes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".