Effect of mobility devices on orientation sensors that contain magnetometers
Bibliographic record
Abstract
Orientation sensors containing magnetometers use the earth's magnetic field as a reference. Ferromagnetic objects may distort this magnetic field, leading to inaccurate orientation output. We explored the viability of these orientation sensors for motion analysis in an assistive mobility device rehabilitative setting. We attached two MTx orientation sensors (XSens; Enschade, the Netherlands), connected to the XBus Master data collection unit (XSens), to a plastic frame such that the relative angle between sensors was constant. We then moved a series of mobility devices in proximity to the plastic frame: two knee-ankle-foot orthoses (aluminum, stainless steel), one ankle-foot orthosis, two transtibial prostheses (exoskeletal, endoskeletal), two walkers (standard, Challenger Low Wide [Evolution Technologies; Port Coquitlam, Canada]), and two wheelchairs (Tango [OrthoFab; Quebec City, Canada], GTi [Quickie; Phoenix, Arizona]). For each mobility device, we calculated the average difference in relative angle between the baseline and peak angles for each of five trials. Errors ranged from less than 0.10 to 35.29 degrees, depending on the mobility device and frame positioning near the device. This demonstrated the large errors that can occur when magnetometer-based orientation sensors with mobility devices are used. While strategic orientation sensor placement on some mobility devices can minimize these errors to an acceptable level, testing protocols should be implemented to verify orientation sensor accuracy for these applications.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".