Touchless gesture recognition system for imaging controls in sterile environment
Bibliographic record
Abstract
Physicians often rely on a patient’s imaging to accurately complete a surgical procedure. To be able to navigate and manipulate these imaging files, physicians resort to using the traditional keyboard and mouse. However, keyboards and computer mice are common mediums for bacterial transfer. As a result, physicians need to re-perform the time-consuming scrubbing techniques after interaction with these devices. With gesture-based control systems becoming an alternative interface over traditional mice and keyboard input systems, new interactive methods can be implemented in a medical environment. Gesture-based interactions allow touchless control of a system that removes the need of the re-sterilization process. This may reduce procedure time and allow the physician to focus on the primary task at hand. We propose a simple method where the primary user can perform most common interactions such as scroll, zoom, pan and window width/level adjustments with just one hand using a Leap Motion® sensor and an open source DICOM viewer, Weasis. The tool, developed as an open-source plugin for the Weasis PACS system, gives the user the ability to use one hand to efficiently manipulate medical imaging data. Our tool can be easily integrated into existing systems, requires no calibration prior to each usage, and is very low cost. An experiment was conducted at a local hospital, with 9 radiologists, 3 surgeons, 3 operating room support staff and 1 engineer to validate the adoptability and usability of our plugin tool. From the results, we can conclude that the participants are receptive to our hand-gesture recognition system as an alternative to using the traditional mouse and keyboard when viewing the imaging or to asking an assistant outside of the sterile field to operate the computer.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".