  Human Activity Recognition has witnessed a significant progress in the last
decade. Although a great deal of work in this field goes in recognizing normal
human activities, few studies focused on identifying motion in sports.
Recognizing human movements in different sports has high impact on
understanding the different styles of humans in the play and on improving their
performance. As deep learning models proved to have good results in many
classification problems, this paper will utilize deep learning to classify
cross-country skiing movements, known as gears, collected using a 3D
accelerometer. It will also provide a comparison between different deep
learning models such as convolutional and recurrent neural networks versus
standard multi-layer perceptron. Results show that deep learning is more
effective and has the highest classification accuracy.
