Abstract
The evaluation of tricks executions in skateboarding is commonly carried out subjectively. The panels of judges rely on their prior experience in classifying the effectiveness of tricks performance during skateboarding competitions. This technique of classifying tricks often fell short in providing accurate evaluations during competition. Therefore, an objective and unbiased means of evaluating skateboarding tricks is non-trivial. This study aims at classifying flat ground tricks namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180 through the use of inertial measurement unit (IMU) and machine learning models. 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 number of features were extracted and engineered. On the pretext of classification models, Support vector machine (SVM), k-NN, artificial neural networks (ANN), logistic regression (LR), random forest (RF) and Naïve Bayes (NB) was employed to identify the type of tricks performed. The results suggest that LR and NB have the highest classification accuracy with 95.0% followed by ANN and SVM together caped at 90.0% and RF and k-NN with 85.0% and 75.0%, respectively. It could be concluded that the proposed method is able to classify the skateboard tricks well. This will assist the judges in providing more accurate evaluations of trick performance as opposed to the subjective and conventional techniques currently applied.
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References
Corrêa, N.K., de Lima, J.C.M., Russomano, T., dos Santos, M.A.: Development of a skateboarding trick classifier using accelerometry and machine learning. Res. Biomed. Eng. 33, 362–369 (2017)
Groh, B.H., Fleckenstein, M., Kautz, T., Eskofier, B.M.: Classification and visualization of skateboard tricks using wearable sensors. Pervasive Mob. Comput. 40, 42–55 (2017)
Groh, B.H., Kautz, T., Schuldhaus, D., Eskofier, B.M.: IMU-based trick classification in skateboarding. In: KDD Workshop on Large-Scale Sports Analytics, p 17 (2015)
Groh, B.H., Fleckenstein, M., Eskofier, B.M.: Wearable trick classification in freestyle snowboarding. In: 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp 89–93. IEEE (2016)
Brock, H., Ohgi, Y.: Assessing motion style errors in Ski jumping using inertial sensor devices. IEEE Sens. J. 17, 3794–3804 (2017). https://doi.org/10.1109/jsen.2017.2699162
Wang, Y., Chen, M., Wang, X., Chan, R.H.M., Li, W.J.: IoT for next-generation racket sports training. IEEE Internet Things J. 5, 4558–4566 (2018)
Gellaerts, J., Bogdanov, E., Dadashi, F., Mariani, B.: In-field validation of an inertial sensor-based system for movement analysis and classification in ski mountaineering. Sensors 18, 885 (2018)
Kos, M., Kramberger, I.: Tennis stroke consistency analysis using miniature wearable IMU. In: 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 1–4. IEEE (2018)
McGrath, J.W., Neville, J., Stewart, T., Cronin, J.: Cricket fast bowling detection in a training setting using an inertial measurement unit and machine learning. J. Sports Sci. 37, pp. 1–7 (2018)
Ahamed, N.U., Kobsar, D., Benson, L., Clermont, C., Kohrs, R., Osis, S.T., Ferber, R.: Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions. PLoS ONE 13, e0203839 (2018)
Worsey, M.T.O., Espinosa, H.G., Shepherd, J.B., Thiel, D.V.: Inertial sensors for performance analysis in combat sports: a systematic review. Sports 7, 28 (2019)
Musa, R.M., Majeed, A.P.P.A., Taha, Z., Chang, S.W., Nasir, A.F.A., Abdullah, M.R.: A machine learning approach of predicting high potential archers by means of physical fitness indicators. PLoS ONE 14, e0209638 (2019)
Taha, Z., Musa, R.M., Abdul Majeed, A.P.P., Alim, M.M., Abdullah, M.R.: The identification of high potential archers based on fitness and motor ability variables: a support vector machine approach. Hum. Mov. Sci. 57, 184–193 (2018). https://doi.org/10.1016/j.humov.2017.12.008
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Abdullah, M.A., Ibrahim, M.A.R., Shapiee, M.N.A.B., Mohd Razman, M.A., Musa, R.M., Abdul Majeed, A.P.P. (2020). The Classification of Skateboarding Trick Manoeuvres Through the Integration of IMU and Machine Learning. In: Jamaludin, Z., Ali Mokhtar, M.N. (eds) Intelligent Manufacturing and Mechatronics. SympoSIMM 2019. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-9539-0_7
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DOI: https://doi.org/10.1007/978-981-13-9539-0_7
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