A Gaussian process regression model for walking speed estimation using a head-worn IMU
Bibliographic record
Abstract
Miniature inertial sensors mainly worn on waist, ankle and wrist have been widely used to measure walking speed of the individuals for lifestyle and/or health monitoring. Recent emergence of head-worn inertial sensors in the form of a smart eyewear (e.g. Recon Jet) or a smart ear-worn device (e.g. Sensixa e-AR) provides an opportunity to use these sensors for estimation of walking speed in real-world environment. This work studies the feasibility of using a head-worn inertial sensor for estimation of walking speed. A combination of time-domain and frequency-domain features of tri-axial acceleration norm signal were used in a Gaussian process regression model to estimate walking speed. An experimental evaluation was performed on 15 healthy subjects during free walking trials in an indoor environment. The results show that the proposed method can provide accuracies of better than around 10% for various walking speed regimes. Additionally, further evaluation of the model for long (15-minutes) outdoor walking trials reveals high correlation of the estimated walking speed values to the ones obtained from fusion of GPS with inertial sensors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".