Accurate foot clearance estimation during level and uneven ground walking using inertial sensors
Bibliographic record
Abstract
Abstract Foot clearance during walking is considered as a key indicator for assessing fall risk, obstacle negotiation strategies and energy expenditure. Foot clearance estimation using inertial measurement units (IMUs) has the advantages of small size, low cost and user-friendliness. However, its application is still limited due to issues with accuracy and reliability. In this paper, we aimed at understanding the limiting factors in foot clearance estimation using low-cost IMUs and proposed a foot clearance estimation method with millimeter-level accuracy. We first analyzed each component in conventional double-integration-based foot clearance estimation, and then proposed a set of new procedures for foot trajectory estimation, including a gait-adaptive complementary filter for orientation estimation, a two-IMU configuration and a shock absorber. Finally, we extracted the foot clearance from the estimated foot trajectory. In the experiments, we recruited eight healthy subjects and instructed them to walk under level and uneven ground conditions; moreover, to validate the applicability of the proposed method, we also instructed the subjects to mimic pathological gaits, including ataxic gait (zigzag walking), waddling gait and Parkinsonian gait. A total of 2640 gait cycles were collected and the extracted foot clearances were benchmarked with the optical motion capture system. With the proposed method, the average mean and standard deviation of the extracted maximal heel clearance and minimal toe clearance in all the gait cycles were −0.34 ± 0.24 cm and 0.02 ± 0.26 cm. The results are more accurate than in previous studies. Most importantly, the proposed method does not require any post correction and flat-floor assumption. The presented foot clearance estimation method provides an applicable and practical clinical solution not only for heel and toe clearance estimation but also for foot trajectory estimation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".