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Identification of Swimmers in Distress Using Unmanned Aerial Vehicles: Experience

at the Mont-Tremblant IRONMAN Triathlon

Department of Emergency Medicine, McGill University, Montreal, Quebec, Canada; bCounty of

Renfrew Paramedic Service, Pembroke, Ontario, Canada;

Funding: This work was supported by the Chaire de recherche en médecine d’urgence, InDro

Robotics, County of Renfrew Paramedic Service, and Groupe Conseil Promutech.

 

Abstract

Background: This preliminary report describes our experience using unmanned aerial vehicles

(UAVs) to identify swimmers in distress at the 2018 Mont-Tremblant IRONMAN triathlon

(Quebec, Canada).

 

Methods: In a prospective pilot study, we sought to determine whether UAV surveillance could identify swimmers showing signs of distress quicker than conventional methods (i.e., lifeguards

on the ground and on watercraft). In addition, we investigated the feasibility of using UAVs for medical surveillance at a triathlon event in terms of operations, costs, safety, legal parameters

and added value. Prior to the race, we screened participants for medical conditions that could elevate their risk of injury during the swim portion of the triathlon. Athletes deemed to be at

increased risk were given a yellow swimming cap to enhance their surveillance by trained observers watching a live video feed from the UAVs.

 

Results: On race day, a total of 3 UAVs (2 mobile, 1 tethered) were launched over Lake Tremblant and provided 3 observers with live video of the swimmers. Of the 2473 race

participants, there were 25 athletes with pre-identified medical conditions who wore a yellow cap during the swim. We did not detect any signs of distress among swimmers wearing yellow caps.

Among the remaining 2448 athletes, there were 5 swimmers who demonstrated signs of distress and required mobilization of water rescue boats; UAV surveillance identified 1 of these 5 distress events before it was seen by lifeguards on rescue boats. None of the athletes in the IRONMAN suffered an adverse event while swimming. Several technical and safety issues related to UAV surveillance arose including poor visibility, equipment loss, and flight autonomy.

Conclusion: While our preliminary findings suggest that using UAVs to identify distressed swimmers during an IRONMAN race is feasible and safe, more research is necessary to

determine how to optimize UAV surveillance at mass sporting events and integrate this

technology within the existing emergency response teams.


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Pitt Meadows Blueberries

The test field chosen for this study is Purewal Blueberry farms, located about 8km west of Coquitlam.

The case study itself will be carried out by performing two identical flights during each visit to the site.

The first flight will be flown when diurnal solar loading results in large temperature differentials between soil, fruit and ambient air – typically early to mid-morning prior to the surrounding soil normalizing to ambient temperature. A thermal infrared (TIR) camera will acquire data during the first flight, which will specifically target data on crop temperature.

A second flight will be flown relatively close to solar noon if possible (in order to minimize the effects of shadow) using a multispectral MicaSense sensor (green, red, red edge, near infrared) in order to collect visual and near infrared wavelengths that will be used to construct the vegetation indices for the project.


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Summerland Cherries

The test field chosen for this study is the Summerland Research and Development Centre (SuRDC). The site is located on the western shore of Okanagan Lake, south of Summerland urban center and bounded on the North and West by Trout Creek.

The case study itself will be carried out by performing two identical flights during each visit to the site.

The first flight will be flown when diurnal solar loading results in large temperature differentials between soil, fruit and ambient air – typically early to mid-morning prior to the surrounding soil normalizing to ambient temperature. A thermal infrared (TIR) camera will acquire data during the first flight, which will specifically target data on crop temperature.

A second flight will be flown relatively close to solar noon if possible (in order to minimize the effects of shadow) using a multispectral MicaSense sensor (green, red, red edge, near infrared) in order to collect visual and near infrared wavelengths that will be used to construct the vegetation indices for the project.