Feasibility of Live Video Feed Transmission from Unmanned Aerial Vehicles for Medical Surveillance During the 2022 Montreal Marathon
Bibliographic record
Abstract
INTRODUCTION: In recent years, unmanned aerial vehicles (UAVs) have been increasingly used for medical surveillance purposes in mass-gathering events. No studies have investigated the reliability of live video transmission from UAVs for accurate identification of distressed race participants in need of medical attention. The aim of this study was to determine the proportion of time during which live medical surveillance UAV video feed was successfully transmitted and considered of sufficient quality to identify acute illness in runners participating in the 2022 Montreal Marathon (Canada). METHODS: Four UAVs equipped with high-resolution cameras were deployed at two pre-defined high-risk areas for medical incidents located within the last 500 meters of the race. The video footage was transmitted in real-time during four consecutive hours to a remote viewing station where four research assistants monitored it on large screens. Interruptions in live feed transmission and moments with inadequate field of view (FOV) on runners were documented. RESULTS: On September 25, 2022, a total of 6,916 athletes ran during the Montreal Marathon and Half Marathon. Out of the eight hours of video footage analyzed (four hours per high-risk area), 91.7% represented uninterrupted live video feed with an adequate view of the runners passing through the high-risk areas. There was a total of 18 live feed interruptions leading to a total interruption time of 22 minutes and 19 seconds (median interruption time of 32 seconds) and eight distinct moments with inadequate FOV on runners which accounted for 17 minutes and 33 seconds (median of 1 minute 47 seconds per moments with inadequate FOV). Active surveillance of drone-captured footage allowed early identification of two race participants in need of medical attention. Appropriate resources were dispatched, and UAV repositioning allowed for real-time viewing of the medical response. CONCLUSION: Live video transmission from UAVs for medical surveillance of runners passing through higher risk segments of a marathon for four consecutive hours is feasible. Live feed interruptions and moments with inadequate FOV could be minimized through practice and additional equipment redundancy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".