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AI, IoT, VR, and the Tour de France

July 6, 2017
The Tour de France may tax bicycle riders, but they will be getting help from AI, IoT, and VR.

The Tour de France (see photo) started this year in Dusseldorf, Germany with 22 teams. Totaling 198 riders, they plan to beat each other to the end of the 3,540-kilometer course that ends in Paris at the Champs-Élysées. Cyclists ride daily legs that climb and descend through mountain chains in the Pyrenees and the Alps. It is physically and mentally challenging, and teamwork can make the difference between winning and losing.

Cyclists obviously do extensive physical training for the event, but because the competition is so fierce, they also look for every  available edge on the equipment front. The bicycles are strong and lightweight, employing the latest in composite technology to reduce the amount of work the cyclist needs to get them and their bike through the course. Carbon fiber frames provide strength and light weight.

Racing helmets are designed for safety, as well as to keep the rider’s head cool. The sleek foam designs provide improved aerodynamic movement. Glasses can also help.

Cyclists are also taking advantage of IoT technology, courtesy of the heart rate monitors tracking the efficiency of their movement. American Andrew Talansky is using Halo Neuroscience’s headset and mobile app during training. It uses a technique called neuropriming with “the process of using electrical stimulation during movement-based training to build stronger, more optimized connections between your brain and muscles.”

Each cyclist’s bike will has a GPS transponder that will generate over 3 billion data points during the Tour. This will be combined with data like the weather and location in real time.

Virtual reality (VR) users will be able to see what cyclists do courtesy of 360-deg. cameras. These are not used during the race, but Team Sky developed a promo 3D video using this technology. There is another reason for recording the course, as cyclists in training can use VR helmets to go over the course multiple times without having to be there. Understanding and learning details about the course provides a rider with an advantage.

Machine learning (ML) comes into play to do predictive analytics—utilizing information such as historic information about the cyclists, the weather, and the current environment—using details like the GPS information from the bikes. This allows the software to address race scenarios like whether the peloton will catch the breakaway riders at various stages of the race. Much of this analysis will be done in the cloud, taking advantage of the parallel upload of data from various sources.

Much of the work that will be done by ML applications is similar to what cyclists and coaches have been doing for decades. Much of the feedback was more limited and usually visual. IoT sensors are providing a lot more data. Radios have already been used to exchange verbal cues, but exchanging data takes this to an even higher level. That burst of speed might not be a natural reaction these days. It could be something with ML and IoT in the mix.


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