A Robust Orientation Filter for Wearable Sensing Applications
Bibliographic record
Abstract
Advancements in the micro-electromechanical systems technology have enabled the realization of small-size, high-performance inertial motion and magnetic field sensors that are embedded in most modern-day smart gadgets. These sensors, when coupled with the high-speed computing and communication technologies may potentially enable in-home monitoring and assessment of human health in the forthcoming age of Smart home technologies, internet-of-thing, and internet-of-everything. However, because the sensor's orientation is generally arbitrary, this may cause erroneous results of important health parameters such as gait speed and range of motion of the knee joint. Therefore, it is important that the sensor's measurements be corrected for orientation. In this work, we designed, implemented, and validated a three-stage sensor fusion algorithm. A gradient descent approach was exploited to estimate the drift in and subtract it from the cumulatively integrated gyroscope data to obtain the orientation in real time. The roll and pitch angles were obtained from the first stage, whereas the second and third stages outputs a coarse and fine estimate of yaw angle, respectively. Since the estimation was obtained primarily from the gyroscope data, the estimated orientation was least affected by the external acceleration and magnetic disturbances. The performance of the proposed algorithm was validated with a publicly available dataset, and in presence of external acceleration and magnetic disturbances. Finally, some key gait parameters were derived from the gait measurements using the proposed filter that showed high conformity to the ground-truth values.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".