Feasibility of Live Video Feed Transmission from UAVs 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. During the 2022 Montreal marathon, the aim of this study was to determine the proportion of data collection time during which live medical surveillance UAV video feed was successfully transmitted and considered of sufficient quality to identify acute illness. Method: Four UAVs equipped with high resolution cameras were deployed at two predefined high-risk areas for medical incidents located within the last 800m 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 on runners were documented. Results: On September 25, 2022, 8,577 athletes registered in 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. The total interruption time was 22 minutes and 19 seconds, and the field of view was considered inadequate for a total of 17 minutes and 33 seconds. Active surveillance of drone-captured footage allowed 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 race for four consecutive hours is feasible. Live feed interruptions and segments with an inadequate field of view could be minimized through practice and additional equipment redundancy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".