Foot Placement Prediction for Assistive Walking by Fusing Sequential 3D Gaze and Environmental Context
Bibliographic record
Abstract
Predicting the locomotion intent of humans is important for controlling assistive robots. Previous studies have investigated assistive walking on structured terrains, but only a few studies have considered rough terrains. Human intent on rough terrains is more difficult to predict because there is a transition at every step. To predict the foot placements of humans on rough terrains, the present paper fuses sequential 3D gaze and the environmental context. The 3D gaze is assumed to be the intersection point of the line of sight as measured by an eye-tracker and the environmental point cloud as measured by an RGBD camera. The sequential 3D gaze and the environmental context are fused based on an RGBD SLAM algorithm. Then the segmented terrain that is closest to the center of sequential 3D gaze is regarded as the most possible foothold area at the next step. Six able-bodied subjects are invited to walk randomly on rough terrains. Their foot placements are labeled and compared with the predicted foot placements. Experimental results show that the proposed method can predict the foot placements of all subjects 0.5 step ahead. With environmental context and user-dependent time window, the distance error of predicting the foot placements can decrease to 0.086 m. Hence, gaze, environmental context, and time window are all important in predicting the human intent when navigating rough terrains.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".