ZeeFi: Zero-Effort Floor Identification with Deep Learning for Indoor Localization
Bibliographic record
Abstract
The knowledge of the floor-level location of a user in a multi-storey building is important for many applications, especially for emergency response. Existing floor identification systems suffer from a variety of limitations such as low accuracy, the need for a time-consuming site survey, assumption of user encounters, knowledge of the initial floor, and/or poor applicability. In this paper, we propose a novel, zero-effort, deep learning-based floor identification system, called ZeeFi. The proposed system uses the widely-available smartphone sensing to identify on which floor a user is located. By recognizing the ground floor automatically, the proposed system does not require site survey, initial floor knowledge, and other assumptions. To achieve accurate floor identification performance, we have developed a deep learning-based method. Experimental results show that the proposed system outperforms the state-of-the-art systems, and is very promising for large-scale deployment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".