Context Aware Mobile Personal Navigation Services Using Multi-Level Sensor Fusion
Bibliographic record
Abstract
Human movement makes personal navigation system (PNS) a challenging topic which as compared to other navigation platforms. Therefore, using customized and context-aware navigation services which are capable of detecting the user activity and device placement is necessary for various aspects of personal mobile navigation services. The main issue in such systems is detecting available context information using embedded mobile sensors in an implicit way. The proposed system in this research can detect user activity modes (e.g.walking, stationary, driving, and etc.) and the placement of the mobile device (e.g. in hand, on the belt, and etc.) to find the most appropriate navigation solution. The context detection algorithm proposed in this study is based on a multi-level sensor fusion algorithm which will improve the current intelligent navigation context detection solutions in the following ways: feature-level integration of multi-sensor data coming from different device sensors such as accelerometer and gyroscope using pattern recognition techniques (e.g. ANN), and high-level information fusion to detect context information from multiple information sources using knowledge discovery techniques (e.g. fuzzy inference system). Extensive pedestrian field tests have been performed using a portable prototype device developed by the MMSS research group at the University of Calgary. These tests have proved that the hybrid multi-level sensor fusion algorithm improves the mean total accuracy from 85% to 97% for user motions and device placement.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".