Adaptive drift calibration of accelerometers with direct velocity measurements
Bibliographic record
Abstract
The accelerometer has become one of the most popular sensors in recent years due to its low cost and the widespread availability of smart phones that now contain three axis accelerometers. This paper proposes an adaptive drift calibration technique for accelerometer signals, correcting higher sampling rate accelerometers using lower sampling rate velocity and position measurements. Specifically, this study made use of 40Hz sampled accelerometer signals captured by smart phones, and corrected them using two different 1Hz sampled velocity reference signals: a vehicle speed sensor and velocity from a Global Positioning System position sensor. The paper compares the performance of two error correction algorithms based on step and ramp shaped error correction delta functions. The ramp function was found to be susceptible to oscillation caused by high frequency noise, while the step function remains stable. The paper also shows that the GPS velocity signal has better performance over the dashboard vehicle velocity signal due to the higher frequency noise within the direct velocity signal.
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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".