Relationships between the number of integrated pressure sensors and step count accuracy using smart insoles
Bibliographic record
Abstract
Several studies have demonstrated that instrumented insoles enable the quantification of steps taken in people with or without walking limitations. However, the number and location of embedded pressure sensors in an insole vary considerably in the literature, resulting in variable step count accuracies. The objective of this study was to determine optimum locations to consider with a minimum pressure sensors (fewer than 5), without altering the accuracy of step detection. An insole was equipped with five pressure sensors (FSRs) located under the heel (FSRH), the first (FSRM1), the third (FSRM3) and the fifth (FSRM5) metatarsal heads, and the great toe (FSRT). Step detection was based on single or combined pressure signals using a step count algorithm, in twelve healthy people who walked during six-minutes at self-selected comfortable speeds. Results showed that there was a statistically significant difference (F=17.8, p<0.05) between the average step count accuracy of single and combinations from two-to-five pressure signals. The best accuracies per sensor combinations were as follows: FSRM1-FSRM5 (99.0±0.9%), FSRM3-FSRM5-FSRT (99.3±0.7%), FSRH-FSRM3-FSRM5-FSRT (99.5±0.4%) and all five FSRs (99.5±0.4%). For a single pressure signal, the best accuracy was 98.0±2.3% with FSRH. These results suggest that the combination of at least two pressure sensors in the insole can yield an accuracy of 99% for step counting in adult people
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".