Locomotor circumvention strategies are altered by stroke: I. Obstacle clearance
Bibliographic record
Abstract
Functional locomotion requires the ability to adapt to environmental challenges such as the presence of stationary or moving obstacles. Difficulties in obstacle circumvention often lead to restricted community ambulation in individuals with stroke. The objective of this study was to contrast obstacle circumvention strategies between post-stroke (n = 12) and healthy individuals (n = 12) performing locomotor and perceptuomotor (joystick navigation) tasks with different obstacle approaches. Participants walked and navigated with a joystick towards a central target, in a virtual environment simulating a large room, while avoiding an obstacle that either remained stationary at the pre-determined point of intersection or moved from head-on or diagonally 30° left/right. The outcome measures included dynamic clearance (DC), instantaneous distance from obstacle at crossing (IDC), number of collisions and preferred side of circumvention. These measures were compared between groups (stroke vs. healthy), obstacle parameter (stationary vs. moving head-on) and direction of approach (left/paretic vs. right/non-paretic). DC was significantly larger when circumventing a moving obstacle that approached head-on as compared to a stationary obstacle for both groups during both tasks, while not significantly different in either diagonal approach in either group. IDC was smaller in the stroke group while walking and larger in both groups during joystick navigation when avoiding moving as compared to stationary obstacle. IDC was significantly larger in the stroke group compared to controls for diagonal approaches during walking, wherein two different strategies emerged amongst individuals with stroke: circumventing to the same (Vsame n = 6) or opposite (Vopp n = 4) side of obstacle approach. This behavior was not seen in the perceptuomotor task, wherein post-stroke participants circumvented to opposite side of the obstacle approach as seen in healthy participants. In the locomotor task, the Vsame subgroup that had greater functional limitations used larger DC as compared to the Vopp subgroup and healthy individuals. The remaining two individuals with stroke collided with obstacles in >50% trials of either obstacle approach. The underlying mechanisms for collision were however different for both individuals. Avoidance strategies in individuals with stroke can vary depending on the individual locomotor capabilities and obstacle characteristics.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".