Difficulty Classification of Mountainbike Downhill Trails Utilizing Deep Neural Networks

Abstract

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.

Data-driven Summarization of Broadcasted Cycling Races by Automatic Team and Rider Recognition

“…barriers and sandpits in cyclocross or gravel segments in road cycling). For the latter type of events, the approach of (Langer et al, 2020) for difficulty classification of mountainbike downhill trails can, for example, be tailored to cyclocross and road cycling segment classification. The work of (Verstockt, 2014) also shows that this is feasible.…”