Design and validation of an inertial measurement unit (IMU)-based sensor for capturing camera movement in the operating room
Bibliographic record
Abstract
Intraoperative surgical video enables better surgical training, continued performance enhancement for surgeons and system-level quality improvement initiatives, however the capture of high-quality intraoperative video of open surgical procedures is difficult. Wearable cameras, typically in the form of a head-mounted action camera are frequently used for this purpose, although the video from these devices often contains significant motion artifact due to movement of the surgeon's head. When trying to compare the performance of various wearable cameras in the surgical setting, we could not find a motion sensor appropriate for this purpose. We therefore describe in this article the design, assembly and validation of a small sensor that can be attached to wearable cameras in the operating room to objectively quantify camera motion. The sensor incorporates an inertial measurement unit coupled to a microcontroller. Concurrent validity is established by comparing the positional sensing of the device to a geared tripod head that allows for fine, measured manipulations of the sensor in three orthogonal axes. The methodology of capturing, processing and reporting camera movement for a surgical procedure is also detailed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".