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Machine Learning-Enabled Classification of Climbing Using Small Data

Authors: Nicholas Milburn, Yu Liang, Dalei Wu

Abstract:

Athlete performance scoring within the climbing domain presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: Classification, climbing, data imbalance, data scarcity, machine learning, time sequence.

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References:


[1] G. Laffaye, G. Levernier, and J.-M. Collin, “Determinant factors in climbing ability: Influence of strength, anthropometry, and neuromuscular fatigue,” Scandinavian Journal of Medicine and Science in Sports, vol. 26, 09 2015.
[2] D. Saul, G. Steinmetz, W. Lehmann, and A. F. Schilling, “Determinants for success in climbing: A systematic review,” Journal of Exercise Science & Fitness, vol. 17, no. 3, pp. 91–100, 2019. (Online) Available: https://www.sciencedirect.com/science/article/pii/S1728869X19300723
[3] C. M. Mermier, J. M. Janot, D. L. Parker, and J. G. Swan, “Physiological and anthropometric determinants of sport climbing performance,” British journal of sports medicine, vol. 34, no. 5, pp. 359–365, 2000.
[4] F. Quaine, L. Martin, and J. Blanchi, “Effect of a leg movement on the organisation of the forces at the holds in a climbing position 3-d kinetic analysis,” Human Movement Science, vol. 16, no. 2, pp. 337–346, 1997, 3-D Analysis of Human Movement - II. (Online) Available: https://www.sciencedirect.com/science/article/pii/S0167945796000607
[5] F. Quaine, L. Martin, and J.-P. Blanchi, “The effect of body position and number of supports on wall reaction forces in rock climbing,” Journal of Applied Biomechanics, vol. 13, no. 1, pp. 14–23, 1997.
[6] F. Fuss and G. Niegl, “Instrumented climbing holds and performance analysis in sport climbing,” Sports Technology, vol. 1, pp. 301 – 313, 03 2009.
[7] A. Dobles, J. C. Sarmiento, and P. Satterthwaite, “Machine learning methods for climbing route classification,” Web link: http://cs229. stanford. edu/proj2017/finalreports/5232206. pdf, 2017.
[8] C. Phillips, L. Becker, and E. Bradley, “strange beta: An assistance system for indoor rock climbing route setting,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 22, no. 1, p. 013130, 2012.
[9] M. Oudah, A. Al-Naji, and J. Chahl, “Hand gesture recognition based on computer vision: A review of techniques,” Journal of Imaging, vol. 6, no. 8, 2020. (Online) Available: https://www.mdpi.com/2313-433X/6/8/73
[10] J. Qi, G. Jiang, G. Li, Y. Sun, and B. Tao, “Intelligent human-computer interaction based on surface emg gesture recognition,” IEEE Access, vol. 7, pp. 61 378–61 387, 2019.
[11] J. A. Hogg, J. Vanrenterghem, T. Ackerman, A.-D. Nguyen, S. E. Ross, R. J. Schmitz, and S. J. Shultz, “Temporal kinematic differences throughout single and double-leg forward landings,” Journal of biomechanics, vol. 99, p. 109559, 2020.
[12] N. Draper, T. Dickson, G. Blackwell, S. Fryer, S. Priestley, D. Winter, and G. Ellis, “Self-reported ability assessment in rock climbing,” Journal of Sports Sciences, vol. 29, no. 8, pp. 851–858, 2011, pMID: 21491325. (Online) Available: https://doi.org/10.1080/02640414.2011.565362
[13] S. H. Hawley, “Panotti: A Convolutional Neural Network Classifier for Multichannel Audio Waveforms,” 4 2018. (Online) Available: https://github.com/drscotthawley/panotti
[14] G. Zerveas, S. Jayaraman, D. Patel, A. Bhamidipaty, and C. Eickhoff, “A transformer-based framework for multivariate time series representation learning,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 2114–2124.
[15] A. Velichko and H. Heidari, “A method for estimating the entropy of time series using artificial neural networks,” Entropy, vol. 23, no. 11, p. 1432, oct 2021. (Online) Available: