On Data Fusion for Orientation Sensing in WBASNs Using Smart Phones
Bibliographic record
Abstract
Orientation sensing is not a new concept. It is being used since ages however, with emergence of new technologies such as Wireless Body Area Sensor Networks (WBASNs), it gives new challenges. Commencement of smart phones that have built in orientation sensors are replacing expensive and complex Inertial Measurement Units (IMUs) designed for a specific purpose. Orientation sensing in WBASN have numerous applications. In e-health applications, rehabilitation investigation of backbone injuries can be measured by continues readings of posture. For that, gyroscopes and accelerometers are key sensors that play vital role. For machines such as robots and air crafts, such data fusion is in practice. However, considering human body movements yet there is a need to find an accurate fusion algorithm that meets all demands with low complexity. In this work, we discussed and compared two algorithms considering Wireless Body Area Sensor Fusion (WBASF) i.e. Kalman and Complementary data fusion techniques. According to our findings, Kalman Filter may have given very good results regarding machines however, Complementary filter proved itself better in performance, complexity and required computational power in WBASNs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".