Graham Smith, Senior Team Lead, Strategic Projects

At NGRAIN we are focused on solving problems in the industrial space, but that doesn’t mean we don’t spend time thinking about how the convergence of Augmented Reality, Big Data and the Internet of Things will affect our daily lives in the future. Lately, while spending hours training for upcoming canoe races, I’ve been thinking about how these technologies might someday change the sport.

I’ve been racing canoes and kayaks since I was a kid, but in the last few years I’ve become involved in a discipline called marathon canoeing. Marathon canoe races typically occur on rivers and last anywhere from a couple hours to several days. As a relative latecomer to the marathon discipline, I lack some of the skills of more experienced paddlers to “read the river”, which means finding the fastest line which in many cases is not the shortest line between two points. Paddlers are always on the lookout for the “good” water (which is generally deep and fast) and want to avoid the “sucky” or “junk” water (medium depth) that tends to bog the canoe down. Paradoxically, very shallow water can also be of benefit to skilled paddlers as they can make use of the waves bouncing off the shallow bottom to accelerate their boat. On a river you paddle on regularly, you tend to know where to find the good water, but what about a race on an unfamiliar river? I imagine a future where technology has a role to play…

For example, assume I am paddling on a river that looked like this:

river1

To me, there are no obvious visual clues as to where the good water is. So, I would likely try to cut the right hand corner fairly tightly to save on distance and hope the water doesn’t get too “sucky”. However, what if I was wearing a pair of sport-ready Augmented Reality glasses (such as the Recon Jet) with an application that could show me where the likely fastest line is, something like this:

augmented1

That was a fairly simple example and in all likelihood very little would have been won or lost based on line choice there, but what if we take a more advanced example such as this:

river2

With a wide river here that splits around an island, there are lots of options and very likely the difference between a bad line and a good line will be significant.

augmented2

Not only are we shown the likely fastest line, we are also shown an area of “sucky” water (in red) that is best avoided and an area of shallow water (in yellow). Knowing where an area of shallow water is before you get to it is a huge advantage as it is much easier to tackle if the boat is accelerated before you are over top of the shallow (especially when racing against other boats).

So, it’s all fine and good to imagine a system that tells me where the fastest water is, but how is the application going to determine the best line? You may have heard of the quantified self, well I’m proposing the sensor-equipped “quantified boat”. This may sound farfetched, but one only needs to look at the cycling world where the use of GPS, heart rate monitors and power meters along with online services such as STRAVA are changing the sport for both professionals and amateurs.

To measure speed, a combination of a GPS (raw speed) and an impeller (boat speed relative to the water) could measure the effect of the water and there are any number of sensors available to measure water depth. As many quantified boats paddle the same section of river more and more data is available and through an analytic step the application is then able to make a prediction of the fastest line from any given location.

In the future, we could take the system to the next level and add cameras to record the water and over time with enough video and geo-synced sensor data the system could be able to predict in real time the fastest line down a river that it has no data on. That would involve a whole lot of real time image processing and predictive analytics and may not be possible today, but the pace at which the field is moving makes me believe that we aren’t that far away from that being possible.

Given the limited size of the market for such a system, I don’t expect to see anything like I’ve envisioned here in the near future, but I will be interested to see how Augmented Reality, Big Data and the IoT change our favourite sports and other aspects of our daily lives.