A Learning-Based Approach Towards Localization of Crowdsourced Motion-Data for Indoor Localization Applications
Bibliographic record
Abstract
Many popular fingerprinting-based indoor localization methods, such as WiFi-based localization systems, rely on a dataset of fingerprints labelled by known locations (fingerprint-location dataset) to be able to localize a user by comparing the queried fingerprint with the fingerprints in the dataset. Generating and updating such a dataset is a burden and requires significant amount of time, human effort and expertise. In this work, we propose a system to build such a dataset from scratch using crowdsourced data. Consequently we reduce the required time and effort, by leveraging the information shared by the crowd. The proposed system is based on localization of user motion, hence called LocaMotion. LocaMotion takes a supervised learning perspective towards localization of user-sent motion data. In other words, LocaMotion is formalised as a classifier that extracts and assigns features from a user motion data to a sequence of points in the building. Training and testing procedures for LocaMotion will be discussed in details followed by extensive experiments to demonstrate its validity and success to localize motion patterns and generate useful fingerprint-location datasets.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".