Bibliographic record
Abstract
With the development of micro-electro-mechanical system (MEMS) technologies, wearable motion sensors (WMSs) have played an increasingly significant role in the design of exoskeletons, where the WMSs are used for motion detection, orientation estimation or position estimation. For these applications, the accuracy of the WMSs will affect the overall performance of the exoskeletons. The purpose of this chapter is to present an calibration platform for assessing the accuracy of WMSs, assisting in developing or choosing proper WMSs for exoskeletons. The presented calibration platform is an instrumented gimbal with three rotation axes. Each axis is equipped with a DC motor and an absolute encoder. Thus, each axis can be controlled independently, and the rotation angle around each axis can be output accurately. In addition, each axis can rotate continuously via the equipped electrical slip rings. One of the major advantages of the instrumented gimbal is that it can be used for accurate motion analysis without needing any additional equipment. In order to verify the function of the platform, validation experiments were conducted, including a static accuracy test and a dynamic accuracy test with and without magnetic disturbances. Results show that the designed gimbal has good potential for evaluating the orientation of WMSs under different conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".