Using Gesture and Speech to Control Surgical Lighting Systems: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Surgical lighting systems (SLSs) provide optimal lighting conditions for operating room personnel. Current systems are mainly adjusted by hand; surgeons either accommodate the light themselves or communicate their requirements to an assistant to ensure optimal surgical conditions. This poses challenges to maintaining sterility, proper accessibility, and illumination and can lead to potential collision problems. Furthermore, the personnel operating the light may not have deep medical knowledge or equipment expertise. OBJECTIVE: This paper introduces a touch-free interaction concept for controlling an SLS using speech and gestures. METHODS: We used an iterative, user-centered design approach with participatory design sessions. This process involved conducting a literature review, several observations of actual surgical sites, and engaging stakeholders through interviews and focus groups. In addition, we carried out 2 user studies: one in a virtual reality setup and another in a living laboratory environment. RESULTS: Our findings indicate that our interaction concept is a viable alternative for controlling an SLS. Despite some technical limitations, surgical experts found the system intuitive and useful, recognizing the significant potential for touch-free lighting adjustments in the operating room. The combination of speech and gesture modalities was seen as helpful and even necessary, with some interactions better suited to one modality over the other. Offering both modalities for each interaction provided greater flexibility. CONCLUSIONS: Our findings suggest that our proposed touch-free interaction concept can enhance surgical conditions and has the potential to replace traditional adjustment.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".