Novel Step Detection Algorithm for Smartphone Indoor Localization Based on CEEMDAN-HT
Bibliographic record
Abstract
Current smartphone-based step detection algorithms do not comprehensively consider the randomness of user movements in daily life, such as carrying a smartphone in various postures, false walking, and walking in various gaits. To overcome the problem, this article proposes a novel step detection algorithm based on complete ensemble empirical mode decomposition with adaptive noise Hilbert transform (CEEMDAN-HT). First, the CEEMDAN partial reconstruction (CEEMDAN-PR) algorithm is presented to reduce noise for inertial data. In order to compensate for the step count misdetection due to the mix-postures. Second, we combine support vector machine (SVM) with CEEMDAN energy entropy for accurate identification of user movement state and smartphone-carrying posture. This approach addresses the issue of false walking and selects appropriate step detection strategies for different scenarios. Finally, HT is used to obtain instantaneous phase information from inertial data for step detection. This approach is capable of adapting to mixed gaits and mixed postures without complex threshold settings. Experiment results show its superior performance, achieving 98.10% average accuracy in free walking and 93.85% in false walking scenarios.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".