Abstract
Advances in sensor technology and big data processing enable new and improved performance analysis of sport athletes. With the increase in data variety and volume, both from on-body sensors and cameras, it has become possible to quantify the specific movement patterns that make a good athlete.
This paper describes Arctic Human Activity Recognition on the Edge (Arctic HARE): a skiing-technique training system that captures movement of skiers to match those against optimal patterns in well-known cross-country techniques. Arctic HARE uses on-body sensors in combination with stationary cameras to capture movement of the skier, and provides classification of the perceived technique. We explore and compare two approaches for classifying data, and determine optimal representations that embody the movement of the skier. We achieve higher than 96% accuracy for real-time classification of cross-country techniques.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI, vol. 16, pp. 265–283 (2016)
Bengio, Y., Grandvalet, Y.: No unbiased estimator of the variance of k-fold cross-validation. J. Mach. Learn. Res. 5, 1089–1105 (2004)
Casale, P., Pujol, O., Radeva, P.: Activity Recognition from Single Chest-Mounted Accelerometer, March 2014. https://www.researchgate.net/publication/260425987_Activity_Recognition_from_Single_Chest-Mounted_Accelerometer
FitnessKeeper, I.: Runkeeper (2018). https://runkeeper.com/. Accessed 21 May 2018
Øyvind Gløersen, Gilgien, M.: Classification of Ski Skating Techniques using the Head’s Trajectory for use in GNSS Field Applications (2016). https://www.researchgate.net/publication/311735263_Classification_of_Ski_Skating_Techniques_using_the_Head’s_Trajectory_for_use_in_GNSS_Field_Applications
Graves, A.: Supervised sequence labelling. In: Supervised Sequence Labelling with Recurrent Neural Networks, pp. 5–13. Springer, Heidelberg (2012)
Halvorsen, P., et al.: Bagadus: an integrated system for arena sports analytics: a soccer case study. In: Proceedings of the 4th ACM Multimedia Systems Conference, pp. 48–59. ACM (2013)
Hidalgo, G.: OpenPose: Real-time multi-person keypoint detection library for body, face, and hands estimation. https://github.com/CMU-Perceptual-Computing-Lab/openpose (2017)
Kishor, N.: Top 8 Technology Trends for 2018 You Must Know About (2018). http://houseofbots.com/news-detail/2653-4-top-8-technology-trends-for-2018-you-must-know-about. Accessed 15 May 2018
Lambert, F.: Tesla’s new head of AI and Autopilot Vision comments on his new role (2017). https://electrek.co/2017/06/21/tesla-ai-autopilot-vision/. Accessed 25 May 2018
Lao, R.: Machine Learning | Accuracy Paradox (2017). https://www.linkedin.com/pulse/machine-learning-accuracy-paradox-randy-lao. Accessed 16 May 2018
McKenney, K.: Cross-country ski techniques with video examples (2014). http://crosscountryskitechnique.com/
Navipedia: Differential gnss (2014). https://gssc.esa.int/navipedia/index.php?title=Differential_GNSS &oldid=13309. Accessed 3 May 2018
O’Donoghue, P.: Research Methods for Sports Performance Analysis (2010)
Rassem, A., El-Beltagy, M., Saleh, M.: Cross-country skiing gears classification using deep learning. arXiv preprint arXiv:1706.08924 (2017)
Rindal, O.M.H., Seeberg, T.M., Tjønnås, J., Haugnes, P., Sandbakk, Ø.: Automatic classification of sub-techniques in classical cross-country skiing using a machine learning algorithm on micro-sensor data. Sensors 18(1), 75 (2017)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)
Software, Q.: Quintic software: Biomechanical 2D Video Analysis (2018). https://www.quinticsports.com/software/. Accessed 21 May 2018
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4 edn. Academic Press (2009)
www.analog.com: Adxl345 (2013). http://www.analog.com/en/products/mems/accelerometers/adxl345.html
www.hudl.com: Hudle: One platform to help the whole team improve (2018). https://www.hudl.com/products/hudl. Accessed 21 May 2018
www.keras.io: Keras: The python deep learning library (2018). https://keras.io/. Accessed 10 Mar 2018
Acknowledgements
This work is partially funded by the Research Council of Norway project number 274451 and Lab Nord-Norge (“Samfunnsløftet”).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nordmo, TA.S., Riegler, M.A., Johansen, H.D., Johansen, D. (2023). Arctic HARE: A Machine Learning-Based System for Performance Analysis of Cross-Country Skiers. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13833. Springer, Cham. https://doi.org/10.1007/978-3-031-27077-2_43
Download citation
DOI: https://doi.org/10.1007/978-3-031-27077-2_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-27076-5
Online ISBN: 978-3-031-27077-2
eBook Packages: Computer ScienceComputer Science (R0)