Accuracy of piezoelectric pedometer and accelerometer step counts
Bibliographic record
Abstract
BACKGROUND: This study aimed to assess step-count accuracy of a piezoeletric pedometer (Yamax PW/EX-510), when worn at different body parts, and a triaxial accelerometer (GT3X+), and to compare device accuracy; and identify the preferred location(s) to wear a pedometer. METHODS: Sixty-three healthy adults (45.8±20.6 years old) wore 7 pedometers (neck, lateral right and left of the waist, front right and left of the waist, front pockets of the trousers) and 1 accelerometer (over the right hip), while walking 120 m at slow, self-preferred/normal and fast paces. Steps were recorded. Participants identified their preferred location(s) to wear the pedometer. Absolute percent error (APE) and Bland and Altman (BA) method were used to assess device accuracy (criterion measure: manual counts) and BA method for device comparisons. RESULTS: Pedometer APE was below 3% at normal and fast paces despite wearing location, but higher at slow pace (4.5-9.1%). Pedometers were more accurate at the front waist and inside the pockets. Accelerometer APE was higher than pedometer APE (P<0.05); nevertheless, limits of agreement between devices were relatively small. Preferred wearing locations were inside the front right (N.=25) and left (N.=20) pockets of the trousers. CONCLUSIONS: Yamax PW/EX-510 pedometers may be preferable than GT3X+ accelerometers to count steps, as they provide more accurate results. These pedometers should be worn at the front right or left positions of the waist or inside the front pockets of the trousers.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".