A concurrent comparison of inertia sensor-based walking speed estimation methods
Bibliographic record
Abstract
This study performed a concurrent comparison of two walking speed estimation methods using shank- and foot-mounted inertial measurement units (IMUs). Based on the cyclic gait pattern of the stance leg during walking, data was segmented into a series of individual stride cycles. The angular velocity and linear accelerations of the shank and foot over each of these cycles were then integrated to determine the walking speed. The evaluation was performed on 10 healthy subjects during treadmill walking where known treadmill speeds were compared with the estimated walking speeds under normal and toe-out walking conditions. Results from the shank-mounted IMU sensor yielded more accurate walking speed estimates, with a maximum root mean square estimation error (RMSE) of 0.09 m/s in normal walking and 0.10 m/s in toe-out conditions; while the foot-mounted IMU sensors yielded a maximum RMSE of 0.14 m/s in normal walking and 0.26 m/s in toe-out conditions. Shank-mounted IMU sensors may prove to be of great benefit in accurately estimating walking speeds in patients whose gait is characterized by abnormal foot motions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".