Cross-Country Skiing Gears Classification using Deep Learning
This work addresses the specific problem of human activity recognition in sports for improving athlete performance, but it is incremental as it applies existing deep learning techniques to a new dataset.
The paper tackled the problem of classifying cross-country skiing movements (gears) using deep learning models, achieving the highest classification accuracy compared to standard methods.
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.