References
- Ball, K. (2008). Biomechanical considerations of distance kicking in Australian Rules football. Sports Biomechanics, 7(1), 10–23. https://doi.org/10.1080/14763140701683015
- Ball, K. (2011). Kinematic comparison of the preferred and non-preferred foot punt kick. Journal of Sports Sciences, 29(14), 1545–1552. https://doi.org/10.1080/02640414.2011.605163
- Bengio, Y. (2013). Deep learning of representations: Looking forward. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7978 LNAI (pp. 1–37). https://doi.org/10.1007/978-3-642-39593-2_1
- Bernardo, J. M., & Smith, A. F. (2009). Bayesian theory. John Wiley & Sons.
- Black, G. M., Gabbett, T. J., Johnston, R. D., Cole, M. H., Naughton, G., & Dawson, B. (2018). A skill profile of the national women’s Australian football league (AFLW). Science and Medicine in Football, 30(June), 1–5. https://doi.org/10.1080/24733938.2018.1489140
- Boyd, L. J., Ball, K., & Aughey, R. J. (2013). Quantifying external load in Australian football matches and training using accelerometers. International Journal of Sports Physiology and Performance, 8(1), 44–51. https://doi.org/10.1123/ijspp.8.1.44
- Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
- Brock, H., & Ohgi, Y. (2017). Assessing motion style errors in ski jumping using inertial sensor devices. IEEE Sensors Journal, 17(12), 3794–3804. https://doi.org/10.1109/JSEN.2017.2699162
- Brock, H., Ohgi, Y., & Lee, J. (2017). Learning to judge like a human: Convolutional networks for classification of ski jumping errors. Proceedings of International Symposium on Wearable Computers (ISWC), pp. 106–113. https://doi.org/10.1145/3123021.3123038
- Buckley, C., O’Reilly, M. A., Whelan, D., Vallely Farrell, A., Clark, L., Longo, V., Gilchrist, M. D., & Caulfield, B. (2017). Binary classification of running fatigue using a single inertial measurement unit. EEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN 2017) (pp. 197–201). https://doi.org/10.1109/BSN.2017.7936040
- Bulling, A., Blanke, U., & Schiele, B. (2014). A tutorial on human activity recognition using body-worn inertial sensors. ACM Computing Surveys, 46(3), 1–33. https://doi.org/10.1145/2499621
- Camomilla, V., Bergamini, E., Fantozzi, S., & Vannozzi, G. (2018). Trends supporting the in-field use of wearable inertial sensors for sport performance evaluation: A systematic review. Sensors (Switzerland), 18(3), 873. https://doi.org/10.3390/s18030873
- Campbell, R., Pease, D., & Cossens, P. (2018). Quantifying landing impacts during a leg strength circuit in male artistic gymnasts - a pilot study. 36th Conference of the International Society of Biomechanics in Sports (pp. 831–834). https://sprinz.aut.ac.nz/__data/assets/pdf_file/0014/203072/194_1331_Campbell.pdf
- Chambers, R. M., Gabbett, T. J., Gupta, R., Josman, C., Bown, R., Stridgeon, P., & Cole, M. H. (2019). Automatic detection of one-on-one tackles and ruck events using microtechnology in rugby union. Journal of Science and Medicine in Sport, 22(7), 827–832. https://doi.org/10.1016/j.jsams.2019.01.001
- Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16(2002), 321–357. https://doi.org/10.1613/jair.953
- Clarke, A. C., Ryan, S., Couvalias, G., Dascombe, B. J., Coutts, A. J., & Kempton, T. (2018). Physical demands and technical performance in Australian Football League Women’s (AFLW) competition match-play. Journal of Science and Medicine in Sport, 21(7), 748–752. https://doi.org/10.1016/j.jsams.2017.11.018
- Conaire, C. Ó., Connaghan, D., Kelly, P., Connor, N. E. O., Gaffney, M., & Buckley, J. (2010). Combining inertial and visual sensing for human action recognition in tennis. Proceedings of the First ACM International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams (pp. 51–56).
- Connaghan, D., Kelly, P., O’Connor, N. E., Gaffney, M., Walsh, M., & O’Mathuna, C. (2011). Multi-sensor classification of tennis strokes. Proceedings of IEEE Sensors, (pp. 1437–1440). IEEE. https://doi.org/10.1109/ICSENS.2011.6127084
- Cust, E. E., Ball, K., Sweeting, A., & Robertson, S. (2019). Biomechanical characteristics of elite female Australian rules football preferred and non-preferred drop punt kicks. Proceedings of the 7th International Conference on Sport Sciences Research and Technology Support (pp. 32–37). https://doi.org/10.5220/0008066300320037
- Cust, E. E., Sweeting, A. J., Ball, K., Anderson, H., & Robertson, S. (2019). The relationship of team and individual athlete performances on match quarter outcome in elite women’s Australian Rules football. Journal of Science and Medicine in Sport, 22(10), 1157–1162. https://doi.org/10.1016/j.jsams.2019.05.004
- Cust, E. E., Sweeting, A. J., Ball, K., & Robertson, S. (2019). Machine and deep learning for sport-specific movement recognition: A systematic review of model development and performance. Journal of Sports Sciences, 37(5), 568–600. https://doi.org/10.1080/02640414.2018.1521769
- Duarte, M. (2014). Detect peaks (Version 1.0.6) [Source code] http://nbviewer.ipython.org/github/demotu/BMC/blob/master/notebooks/DetectPeaks.ipynb%0A
- Ellens, S., Blair, S., Peacock, J., Barnes, S., & Ball, K. (2017). Use of accelerometers in Australian Football to identify a kick. 35th International Conference on Biomechanics in Sport (pp. 218–221).
- Faulkner, H., & Dick, A. (2015). AFL player detection and tracking. 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–8). https://doi.org/10.1109/DICTA.2015.7371226
- Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
- Forman, G., & Scholz, M. (2010). Apples-to-apples in cross-validation studies: Pitfalls in classifier performance measurement. ACM SIGKDD Explorations Newsletter, 12(1), 49–57. https://doi.org/10.1145/1882471.1882479
- Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O’Reilly Media, Inc.
- Groh, B. H., Fleckenstein, M., Kautz, T., & Eskofier, B. M. (2017). Classification and visualization of skateboard tricks using wearable sensors. Pervasive and Mobile Computing, 40(September 2017), 42–55. https://doi.org/10.1016/j.pmcj.2017.05.007
- Hand, D. J., & Till, R. J. (2001). A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine Learning, 45(2), 171–186. https://doi.org/10.1023/A:1010920819831
- Harding, J. W., Small, J. W., & James, D. A. (2008). Feature extraction of performance variables in elite half-pipe snowboarding using body mounted inertial sensors. BioMEMS and Nanotechnology III, 1–12. https://doi.org/10.1117/12.759259
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference and prediction (Second ed.). Springer. https://doi.org/10.1007/978-0-387-84858-7
- He, H., Bai, Y., Garcia, E., & Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence) (pp. 1322-1328). IEEE. https://doi.org/10.1109/IJCNN.2008.4633969
- Holzemann, A., & Van Laerhoven, K. (2018). Using wrist-worn activity recognition for basketball game analysis. Proceedings of the 5th International Workshop on Sensor-Based Activity Recognition and Interaction. https://doi.org/10.1145/3266157.3266217
- Jiao, L., Bie, R., Wu, H., Wei, Y., Ma, J., Umek, A., & Kos, A. (2018). Golf swing classification with multiple deep convolutional neural networks. International Journal of Distributed Sensor Networks, 14(10), 10. https://doi.org/10.1177/1550147718802186
- Kautz, T., Groh, B. H., Hannink, J., Jensen, U., Strubberg, H., & Eskofier, B. M. (2017). Activity recognition in beach volleyball using a deep convolutional neural network. Data Mining and Knowledge Discovery, 31(6), 1678–1705. https://doi.org/10.1007/s10618-017-0495-0
- Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
- Lemaître, G., Nogueira, F., & Aridas, C. K. (2017). Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning. Journal of Machine Learning Research, 18(17), 1–5. http://jmlr.org/papers/v18/16-365.html
- Ling, C. X., Huang, J., & Zhang, H. (2003). AUC: A statistically consistent and more discriminating measure than accuracy. International Joint Conference on Artificial Intelligence (IJCAI), 3, 519–524.
- Mannini, A., & Sabatini, A. M. (2010). Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors, 10(2), 1154–1175. https://doi.org/10.3390/s100201154
- McKinney, W. (2010). Data structures for statistical computing in python. Proceedings of the 9th Python in Science Conference, 445, 51–56.
- Mehta, S. (2019). Relationship between workload and throwing injury in varsity baseball players. Physical Therapy in Sport, 40, 66–70. https://doi.org/10.1016/j.ptsp.2019.08.001
- Parrington, L., Phillips, E., Wong, A., Finch, M., Wain, E., & MacMahon, C. (2016). Validation of inertial measurement units for tracking 100m sprint data. 34th International Conference of Biomechanics in Sports (pp. 442–445).
- Peacock, J. C. A., & Ball, K. (2018a). Is there a sweet spot on the foot in Australian football drop punt kicking? Journal of Sports Sciences, 37(4), 467–476. https://doi.org/10.1080/02640414.2018.1505408
- Peacock, J. C. A., & Ball, K. (2018b). Kick impact characteristics of accurate Australian football drop punt kicking. Human Movement Science, 61(July), 99–108. https://doi.org/10.1016/j.humov.2018.07.009
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12(2011), 2825–2830. https://doi.org/10.1007/s13398-014-0173-7.2
- Phillips, E., Farrow, D., Ball, K., & Helmer, R. (2013). Harnessing and understanding feedback technology in applied settings. Sports Medicine, 43(10), 919–925. https://doi.org/10.1007/s40279-013-0072-7
- Punchihewa, N. G., Yamako, G., Fukao, Y., & Chosa, E. (2019). Identification of key events in baseball hitting using inertial measurement units. Journal of Biomechanics, 87, 157–160. https://doi.org/10.1016/j.jbiomech.2019.02.001
- Python Software Foundation. (2018). Python programming language (Version 3.6.6)[Computer software]. Python Software Foundation. https://www.python.org/
- Robertson, S., Gupta, R., & McIntosh, S. (2016). A method to assess the influence of individual player performance distribution on match outcome in team sports. Journal of Sports Sciences, 34(19), 1893–1900. https://doi.org/10.1080/02640414.2016.1142106
- Schuldhaus, D., Zwick, C., Körger, H., Dorschky, E., Kirk, R., & Eskofier, B. M. (2015). Inertial sensor-based approach for shot/pass classification during a soccer match. 21st ACM KDD Workshop on Large-Scale Sports Analytics (pp. 1–4). https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2015/Schuldhaus15-ISA.pdf
- Seshadri, D. R., Li, R. T., Voos, J. E., Rowbottom, J. R., Alfes, C. M., Zorman, C. A., & Drummond, C. K. (2019). Wearable sensors for monitoring the physiological and biochemical profile of the athlete. Npj Digital Medicine, 2(1), 72. https://doi.org/10.1038/s41746-019-0150-9
- Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. Advances in Neural Information Processing Systems, 2951–2959. https://papers.nips.cc/paper/2012/file/05311655a15b75fab86956663e1819cd-Paper.pdf
- Thomas, G., Gade, R., Moeslund, T. B., Carr, P., & Hilton, A. (2017). Computer vision for sports: Current applications and research topics. Computer Vision and Image Understanding. 159(04), 011. https://doi.org/10.1016/j.cviu.2017.04.011
- Walker, C., Sinclair, P., Graham, K., & Cobley, S. (2017). The validation and application of inertial measurement units to springboard diving. Sports Biomechanics, 16(4), 485–500. https://doi.org/10.1080/14763141.2016.1246596
- Wang, Y., Zhao, Y., Chan, R. H. M., & Li, W. J. (2018). Volleyball skill assessment using a single wearable micro inertial measurement unit at wrist. IEEE Access, 6(2018), 13758–13765. https://doi.org/10.1109/ACCESS.2018.2792220
- Whiteside, D., Cant, O., Connolly, M., & Reid, M. (2017). Monitoring hitting load in tennis using inertial sensors and machine learning. International Journal of Sports Physiology and Performance, 12(9), 1212–1217. https://doi.org/10.1123/ijspp.2016-0683
- Wundersitz, D. W. T., Josman, C., Gupta, R., Netto, K. J., Gastin, P. B., & Robertson, S. (2015). Classification of team sport activities using a single wearable tracking device. Journal of Biomechanics, 48(15), 3975–3981. https://doi.org/10.1016/j.jbiomech.2015.09.015