Synergism of INS and PDR in Self-Contained Pedestrian Tracking With a Miniature Sensor Module
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents a sensor-based pedestrian tracking technology that does not rely on any infrastructure. The information about human walking is monitored by a sensor module composed of accelerometers, gyroscopes and magnetometers. The acquired information is used by an algorithm proposed in this paper to accurately compute the position of a pedestrian. Through the application of human kinetics, the algorithm integrates two traditional technologies: strap-down inertial navigation and pedestrian dead-reckoning. Based on the algorithm, this paper presents several methods to improve the accuracy of pedestrian tracking through reducing the integral drift which is the main cause of errors in inertial navigation. These methods have been carefully investigated through theoretical study, simulation and field experiment. The results indicate accurate tracking is achievable through the application of both the proposed algorithm and methods. Evidently, it is feasible to develop self-contained pedestrian tracking system using inertial/magnetic sensors, eliminating the need for complicated and normally expensive infrastructure that most existing tracking systems rely on. </para>
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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 | 0.001 |
| 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 it