A Simple 2D Wheelchair Positioning: Correcting IMU’s Data Using One Encoder and Applying Kalman Filtering
Bibliographic record
Abstract
Inertial Measurement Units (IMU) are widely used for spatial positioning. They are well known, however, for signal drift. A common way of overcoming the drift is to use Kalman Filtering. In this study, we have undertaken some experiments during wheelchair propulsion, recording data with an IMU, an Encoder (tachometer) and an Optotrak (motion analysis system). We then applied Kalman filtering (with two approaches) to IMU’s data. Eventually, in order to verify Kalman’s results, they were compared to Optotrak’s data. As result of this study, 2D wheelchair tracking can be done with acceptable precision, using one IMU and one Encoder and applying Kalman filtering. Kalman filtering with approach B was a better predictor of subject’s spatial position than approach A. Kalman and even IMU’s results for rotation were of good accuracy; therefore IMU’s data can be used to find all angular characteristics of subject’s position, even without applying Kalman filtering, if the offsets are precisely found through a stationary test.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".