The effect of the presence of physical obstacles during blind navigation
Bibliographic record
Abstract
Locomotion is essential in daily life. Past research has shown that healthy subjects are successful in reaching a short-distance destination without vision (Thomson 1980). The objective of this study is to describe reaction to the known presence of physical obstacles when navigating in a straight or diagonal path without vision for 8 meters. Ten healthy subjects (19-23 years old) participated in this study. Kinematic data were collected with a Vicon Motion Analysis System including 8 cameras, with a full-body marker set. Pool noodles were used to create physical obstacles. For the total distance travelled by the subject, there was no significant differences between the straight path and the diagonal path [F(2,18) = 0.04, p > 0.05]. Also, a significant difference was found between the presence of zero, one or two obstacles [F (2, 18) = 17.85, p < 0.05]. For the final angular deviation of the subject, there was no significant differences between straight path and diagonal path [F(1,9) = 3.489, p > 0.05 ]. There was a close to significant difference between the presence versus absenceof obstacles [ F(2,18) = 3.368, p= 0.057]. Without obstacle, subjects undershoot the target. However, we found that for every obstacle added, there was an impact on the distance travelled, as subjects walked further.Acknowledgments: CIHR, Physiotherapy Foundation of Canada and University of Ottawa
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| 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".