Welcome to my talk about adequate risk indication in mountain biking.
My name is Stefan Langer from the LMU Munich and in the first screen here you can see a
mountain bike rider and she is getting a notification on her smart watch that tells
her that she is riding in a scenario that is of untypically high risk for her skill set.
In the middle there is a overall risk indication metric that tells her
how risky the application considers this scenario, on the top you can see internal risk
and on the bottom you can see external risk. And from this point on she can decide on her own
whether she wants to keep going or maybe walk her bike, go slower, or take a reroute.
Why would you need such an application? As you all know, outdoor sports are currently
thriving and not only caused by the pandemic. People have been trying to get away from their
desks towards the outdoors for many years now. And while some of them
start sports like running or hiking, which I would consider of not being of much risk,
some of them try sports like mountain biking and those often result in injuries and crashes.
For example, I myself had a big mountain bike crash about 4 years ago
and it was mostly caused by inattention. I was riding from one side of the mountain
to the other side on a flat and very easy path and I was just cruising along, not paying attention,
and suddenly crashed, which resulted in three years of surgeries and struggle.
What we want to do with this work is: Create a concept for an application,
that can tell you your current risk and warn you of potentially risky scenarios.
Those are the bits and pieces that go into such a risk indication application.
On the top we have three types of risk factors. The internal risk factors,
which are stress, fatigue or fitness. For example if you are facing a lot of
chronic stress, you might be distracted from the task at hand, which could be mountain biking.
You have your external risk factors like difficulty and surface type.
For example, if you are riding your bike on a flat and paved road, you are not very likely
to crash, but if you are riding your bike down a very steep and technical and lose trail,
you are more likely to crash, obviously. And then you have your mixed risk factors. You
have for example a difficulty to skill ratio. In my exmaple, I was riding on a path,
that was too easy I would say and not paying attention. So what if the trail
would have been harder?Would I have paayed more attention and not crashed? I don't know.
And then, to bring all those parts together we need a risk indication application that
accumulates all those factord and communicates that risk to the rider.
How could such an application look like? The frist two screens are pretty straight forward.
You start your activity like you would usually start your activity tracking on your smart watch,
then you have the screen that we saw in the start frame and then as you soon as you stop the
application you can consider your current risk and tell the system whether it was of potentially high
risk, moderate risk or low risk. And from this point on the application can decide, whether it
wants to adjust the algorithm and maybe detect factors that make risk more intense for you.
How can we solve this in the future? For internal risk factors we have a lot
of data in those smart watches already. We have external risk factors,
where we already detected difficulty and surface type with machine learning algorithms
and then we have mixed risk factors, like difficulty to skill ratio.
For that purpose, we are recording a data set from which we can hopefully detect
mismatches in difficulty to skill or maybe detect fear or other indicators.
And then we need a risk indication application, that could, for example, in a first step, be
a Wizard of Oz style application. Thank you for your attention.