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Difficulty Classification of Mountainbike Downhill Trails Utilizing Deep Neural Networks
- Source :
- Machine Learning and Knowledge Discovery in Databases ISBN: 9783030438869, PKDD/ECML Workshops (2)
- Publication Year :
- 2020
- Publisher :
- Springer International Publishing, 2020.
-
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.
- Subjects :
- Computer science
business.industry
Subjective perception
Deep learning
Pattern recognition
02 engineering and technology
010501 environmental sciences
Sports analytics
Accelerometer
01 natural sciences
Convolutional neural network
Colored
0202 electrical engineering, electronic engineering, information engineering
Deep neural networks
020201 artificial intelligence & image processing
Artificial intelligence
business
Categorical variable
0105 earth and related environmental sciences
Subjects
Details
- ISBN :
- 978-3-030-43886-9
- ISBNs :
- 9783030438869
- Database :
- OpenAIRE
- Journal :
- Machine Learning and Knowledge Discovery in Databases ISBN: 9783030438869, PKDD/ECML Workshops (2)
- Accession number :
- edsair.doi...........445e3dae9e8e4e9c13acbe169485ce0e