Walking along curved paths of different angles: the relationship between head and trunk turning
Bibliographic record
Abstract
Walking along a curved path requires coordinated motor actions of the entire body. Here, we investigate the relationship between head and trunk movements during walking. Previous studies have found that the head systematically turns into turns before the trunk does. This has been found to occur at a constant distance rather than at a constant time before a turn. We tested whether this anticipatory head behavior is spatially invariant for turns of different angles. Head and trunk positions and orientations were measured while participants walked around obstacles in 45 degrees, 90 degrees, 135 degrees or 180 degrees turns. The radius of the turns was either imposed or left free. We found that the head started to turn into the direction of the turn at a constant distance before the obstacle (approximately 1.1 m) for turn angles up to 135 degrees . During turns, the head was consistently oriented more into the direction of the turn than the trunk. This difference increased for larger turning angles and reached its maximum later in the turn for larger turns. Walking speeds decreased monotonically for increasing turn angles. Imposing fixed turn radii only affected the point at which the trunk started to turn into a turn. Our results support the view that anticipatory head movements during turns occur in order to gather advance visual information about the trajectory and potential obstacles.
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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 | 0.004 |
| 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 | 0.001 | 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".