# Difficulty Classification of Mountainbike Downhill Trails utilizing Deep Neural Networks

## TL;DR

This paper presents a novel deep learning method that classifies mountainbike downhill trail difficulty levels using sensor data, achieving over 90% accuracy, and addresses inconsistencies in traditional grading scales.

## Contribution

It introduces the first computational approach for classifying trail difficulty using sensor data and deep neural networks, improving consistency and objectivity.

## Key findings

- Achieved a maximum accuracy of 0.9097 in difficulty classification.
- Used sensor data from accelerometers and gyroscopes for model training.
- Demonstrated the feasibility of automated trail difficulty assessment.

## 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.