Adaptation and post-adaptation effects of haptic forces on locomotion in healthy young adults
Bibliographic record
Abstract
BACKGROUND: Developing rehabilitation strategies to improve functional walking and postural control in patients is a priority for rehabilitation clinicians and researchers alike. One possible strategy is the use of sensory modalities to elicit adaptive locomotor gait patterns. This study aimed to explore to what extent haptic inputs, in the form of forward-leading tensile forces delivered to the hand, compared to no force, may lead to adaptation and post-adaptation effects on gait parameters, during and after the haptic exposure, respectively. METHODS: Thirteen healthy young individuals were recruited for this study. We developed an innovative system combining virtual reality and haptic tensile forces in the direction of locomotion to simulate walking with a dog. A robotic arm generated forces via an adapted leash to the participant's hand while they walked on a self-paced treadmill immersed in a virtual environment with scene progression synchronized to the treadmill. RESULTS: All participants showed significant increases in instantaneous gait velocity and stride length, with accompanying decreases in double-limb support time (p < 0.05) when walking with a haptic tensile force of either 10 or 20 N, relative to pre-force epoch levels, indicating an adaptation effect. When the 10 or 20 N force was removed, gait measures generally remained changed relative to baseline pre-force levels (p < 0.05), providing evidence of a post-adaptation effect. CONCLUSIONS: Changes in spatiotemporal outcomes provide evidence that both adaptation and post-adaptation effects were present in response to the application and removal of a haptic force. Future studies will investigate whether similar changes in elderly and post-stroke populations can be actualized during steady-state walking.
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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) | 0.002 | 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".