  The difficulty of mountainbike downhill trails is a subjective perception.
However, sports-associations and mountainbike park operators attempt to group
trails into different levels of difficulty with scales like the
Singletrail-Skala (S0-S5) or colored scales (blue, red, black, ...) as proposed
by The International Mountain Bicycling Association. Inconsistencies in
difficulty grading occur due to the various scales, different people grading
the trails, differences in topography, and more. We propose an end-to-end deep
learning approach to classify trails into three difficulties easy, medium, and
hard by using sensor data. With mbientlab Meta Motion r0.2 sensor units, we
record accelerometer- and gyroscope data of one rider on multiple trail
segments. A 2D convolutional neural network is trained with a stacked and
concatenated representation of the aforementioned data as its input. We run
experiments with five different sample- and five different kernel sizes and
achieve a maximum Sparse Categorical Accuracy of 0.9097. To the best of our
knowledge, this is the first work targeting computational difficulty
classification of mountainbike downhill trails.
