Bibliographic record
Abstract
Recent advancements in low-power, low-cost, miniaturized sensor technology have propelled wearable electronics into the mainstream.Wearable devices like smartwatches, smartglasses and other smart clothing are now commonplace, finding applications in health and fitness, sports analytics, personnel tracking, and human computer interfaces.Many of these systems, however, are consumer oriented, with closed environments and limited access to sensor data, greatly restricting their use as exploratory tools for experimenters and researchers.Some research devices exist but are often expensive and limited in what they offer.In this work, we describe the development of a novel wearable sensor platform targeted towards researchers, developers, and hobbyists, that attempts to overcome many of the limitations of existing systems while adding new features to improve and simplify experimental designs.Following a clear set of guidelines, the system was built, validated, and tested in a real-life experiment to assess its effectiveness and ease of use.The developed platform consists of small 35x25x15mm nodes each containing an nRF52 Bluetooth microcontroller, IMU sensor, and small LiPo battery.Two additional input and output nodes allow external devices and sensor connections.The nodes communicate wirelessly with a central hub using Bluetooth 5, sending raw sensor data, which is then visualized and analyzed using a graphical software interface.xi 8.7 Plot of the ECG and Heart Rate during the drive . . . . . . . . . . . . .8.8 Heart rate percentage change . . . . . . . . . . . . . . . . . . . . . . . .8.9 Motion measured when the driver looks both ways . . . . . . . . . . . . .8.10 Averaged head motion . . . . . . . . . . . . . . . . . . . . . . . . . . . .8.11 Average head motion curves across all trials . . . . . . . . . . . . . . . .8.12 Foot motion events throughout the drive . . . . . . . . . . . . . . . . . .8.13 Comparison of the different stop durations . . . . . . . . . . . . . . . . .8.14 Acceleration pattern of the hand's motion during the right turn . . . . .8.15 Extraction of turn parameters . . . . . . . . . . . . . .
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".