Abstract
This study aims to improve classification accuracy of different Support Vector Machine (SVM) models in classifying flat ground tricks namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180 through the identification of significant time-domain features. An amateur skateboarder (23 years of age ±5.0 years’ experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. From the IMU data a total of 36 features were extracted through statistical measures. The significant features were identified through two feature selection methods, namely Pearson and Chi-Squared. The variation of the SVM models (kernel-based) was evaluated both on all features and selected features in classifying the skateboarding tricks. It was shown from the study that all classifiers improved significantly in terms of training accuracy, prediction speed, training time and test accuracy. The Cubic-based SVM and Quadratic-based SVM demonstrated a 100% accuracy on both the test and train dataset, however, the Cubic-based SVM model provided the fastest training time and prediction speed between the two models. It could be concluded that the proposed method is able to improve the classification of the skateboarding tricks well.
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
References
Corrêa NK, de Lima JCM, Russomano T, dos Santos MA (2017) Development of a skateboarding trick classifier using accelerometry and machine learning. Res Biomed Eng 33:362–369
Anand A, Sharma M, Srivastava R, Kaligounder L, Prakash D (20177) Wearable motion sensor based analysis of swing sports. In: 2017 16th IEEE international conference on machine learning and applications (ICMLA). IEEE (2017) pp 261–267
Büthe L, Blanke U, Capkevics H, Tröster G (2016) A wearable sensing system for timing analysis in tennis. In: 2016 IEEE 13th international conference on wearable and implantable body sensor networks (BSN). IEEE, pp 43–48
Connaghan D, Kelly P, O’Connor NE, Gaffney M, Walsh M, O’Mathuna C (2011) Multi-sensor classification of tennis strokes. In: SENSORS, 2011 IEEE. IEEE, pp 1437–1440
Adelsberger R, Tröster G (2013) Experts lift differently: classification of weight-lifting athletes. In: 2013 IEEE international conference on body sensor networks. IEEE, pp 1–6
Groh BH, Fleckenstein M, Eskofier BM (2016) Wearable trick classification in freestyle snowboarding. In: BSN 2016—13th annual body sensor networks conference pp 89–93
Brock H, Ohgi Y (2017) Assessing motion style errors in Ski jumping using inertial sensor devices. IEEE Sens J 17:3794–3804
Brock H, Ohgi Y, Lee J (2017) Learning to judge like a human: convolutional networks for classification of ski jumping errors. Proceeding—International symposium on wearable computing ISWC. Part F1305, 106–113 (2017)
Groh BH, Kautz T, Schuldhaus D (2015) IMU-based trick classification in skateboarding, KDD work. Large-Scale Sport, Anal
Groh BH, Fleckenstein M, Kautz T, Eskofier BM (2017) Classification and visualization of skateboard tricks using wearable sensors. Pervasive Mob Comput 40:42–55
Anlauff J, Weitnauer E, Lehnhardt A, Schirmer S, Zehe S, Tonekaboni K (2010) A method for outdoor skateboarding video games. ACM International Conference Proceeding Series, pp 40–43
Abdullah MA, Ibrahim MAR, Shapiee MNAB, Mohd Razman MA, Musa RM, Abdul Majeed APP (2020) The classification of skateboarding trick manoeuvres through the integration of IMU and machine learning. Lect Notes Mech Eng 67–74
Acknowledgements
The authors would like to acknowledge the Ministry of Education, Malaysia and Universiti Malaysia Pahang for supporting and funding this research via FRGS/1/2019/TK03/UMP/02/6 (RDU1901115).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Abdullah, M.A. et al. (2020). The Classification of Skateboarding Tricks by Means of Support Vector Machine: An Evaluation of Significant Time-Domain Features. In: Mohd Razman, M., Mat Jizat, J., Mat Yahya, N., Myung, H., Zainal Abidin, A., Abdul Karim, M. (eds) Embracing Industry 4.0. Lecture Notes in Electrical Engineering, vol 678. Springer, Singapore. https://doi.org/10.1007/978-981-15-6025-5_12
Download citation
DOI: https://doi.org/10.1007/978-981-15-6025-5_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6024-8
Online ISBN: 978-981-15-6025-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)