Automatic Classification of Locomotion in Sport: A Case Study from Elite Netball.

Ahmadi, A., Mitchell, E., Richter, C., Destelle, F., Gowing, M., O’Connor, N. E., & Moran, K. (2015). Toward Automatic Activity Classification and Movement Assessment During a Sports Training Session. IEEE Internet of Things Journal, 2(1), 23-32. doi:10.1109/jiot.2014.237723810.1109/JIOT.2014.2377238Search in Google Scholar

Ahmadi, A., & Rowlands, D. (2010). Development of inertial and novel marker-based techniques and analysis for upper arm rotational velocity measurements in tennis. Sports Engineering, 12, 179-188. doi:10.1007/s12283-010-0044-110.1007/s12283-010-0044-1Search in Google Scholar

Altun, K., Barshan, B., & Tunçel, O. (2010). Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognition, 43(10), 3605-3620. doi:10.1016/j.patcog.2010.04.01910.1016/j.patcog.2010.04.019Search in Google Scholar

Amancio, D. R., Comin, C. H., Casanova, D., Travieso, G., Bruno, O. M., Rodrigues, F. A., & Costa Lda, F. (2014). A systematic comparison of supervised classifiers. PLoS One, 9(4), e94137. doi:10.1371/journal.pone.009413710.1371/journal.pone.0094137399894824763312Search in Google Scholar

Anguita, D., Ghio, A., Oneta, L., Parra Parez, X., & Reyes Ortiz, J. (2013). A Public Domain Dataset for Human Activity Recognition Using Smartphones. Paper presented at the European Symposium On Artificaial Neural Networks, Computer Intelligence and Machine Learning, Bruges.Search in Google Scholar

Bailey, J. A., Gastin, P. B., Mackey, L., & Dwyer, D. B. (2017). The Player Load Associated With Typical Activities in Elite Netball. Int J Sports Physiol Perform, 12(9), 1218-1223. doi:10.1123/ijspp.2016-037810.1123/ijspp.2016-037828182504Search in Google Scholar

Bao, L., & Intille, S. (2004). Activity Recognition from User-Annotated Acceleration Data. Paper presented at the Pervasive Computing 2004.10.1007/978-3-540-24646-6_1Search in Google Scholar

Barris, S., & Button, C. (2008). A Review of Vision-Based Motion Analysis in Sport. Sports Medicine, 38(12), 1025-1043.10.2165/00007256-200838120-0000619026019Search in Google Scholar

Barshan, B., & Murat, C. Y. (2014). Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units. The Computer Journal, 57(11), 1649-1657. doi:10.1112/comjnl/bxt075Search in Google Scholar

Bourdon, P. C., Cardinale, M., Murray, A., Gastin, P., Kellmann, M., Varley, M. C., . . . Cable, N. T. (2017). Monitoring Athlete Training Loads: Consensus Statement. Int J Sports Physiol Perform, 12(Suppl 2), S2161-S2170. doi:10.1123/IJSPP.2017-020810.1123/IJSPP.2017-020828463642Search in Google Scholar

Bouten, C. V., Koekkoek, K. T., Verduin, M., Kodde, R., & Janssen, J. D. (1997). A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Trans Biomed Eng, 44(3), 136-147. doi:10.1109/10.55476010.1109/10.5547609216127Search in Google Scholar

Boyd, L., Ball, K., & Aughey, R. J. (2011). Reliability of minimxX accelerometers for measuring physical activity in Australian football. International Journal of Physiology and Performance, 6(3), 311-321.Search in Google Scholar

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. doi:10.1145/249962110.1145/2499621Search in Google Scholar

Buttfield, A., & Ball, K. (2019). The practical application of a method of analysing the variability of within-step accelerations collected via athlete tracking devices. J Sports Sci, 1-8. doi:10.1080/02640414.2019.169998710.1080/02640414.2019.169998731809646Search in Google Scholar

Capela, N. A., Lemaire, E. D., & Baddour, N. (2015). Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients. PLoS One, 10(4), e0124414. doi:10.1371/journal.pone.012441410.1371/journal.pone.0124414440145725885272Search in Google Scholar

Chambers, R. M., Gabbett, T. J., & Cole, M. H. (2019). Validity of a Microsensor-Based Algorithm for Detecting Scrum Events in Rugby Union. Int J Sports Physiol Perform, 14(2), 176-182. doi:10.1123/ijspp.2018-022210.1123/ijspp.2018-022230039994Search in Google Scholar

Chambers, R. M., Gabbett, T. J., Cole, M. H., & Beard, A. (2015). The Use of Wearable Microsensors to Quantify Sport-Specific Movements. Sports Med, 45(7), 1065-1081. doi:10.1007/s40279-015-0332-910.1007/s40279-015-0332-925834998Search in Google Scholar

Chardonnens, J., Favre, J., Le Callennec, B., Cuendet, F., Gremion, G., & Aminian, K. (2012). Automatic measurement of key ski jumping phases and temporal events with a wearable system. J Sports Sci, 30(1), 53-61. doi:10.1080/02640414.2011.62453810.1080/02640414.2011.62453822168430Search in Google Scholar

Chen, C., Jafari, R., & Kehtarnavaz, N. (2015). A survey of depth and inertial sensor fusion for human action recognition. Multimedia Tools and Applications, 76(3), 4405-4425. doi:10.1007/s11042-015-3177-110.1007/s11042-015-3177-1Search in Google Scholar

Colyer, S. L., Evans, M., Cosker, D. P., & Salo, A. I. T. (2018). A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System. Sports Med Open, 4(1), 24. doi:10.1186/s40798-018-0139-y10.1186/s40798-018-0139-y598669229869300Search in Google Scholar

Coutts, A. J., & Duffield, R. (2010). Validity and reliability of GPS devices for measuring movement demands of team sports. J Sci Med Sport, 13(1), 133-135. doi:10.1016/j.jsams.2008.09.01510.1016/j.jsams.2008.09.01519054711Search in Google Scholar

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. J Sports Sci, 37(5), 568-600. doi:10.1080/02640414.2018.152176910.1080/02640414.2018.152176930307362Search in Google Scholar

Docherty, D., & Neary, P. (1988). Time-motion analysis related to the physiological demands of rugby. Journal of Human Movement Studies, 14, 269-277.Search in Google Scholar

Erdaş, Ç. B., Atasoy, I., Açıcı, K., & Oğul, H. (2016). Integrating Features for Accelerometer-based Activity Recognition. Procedia Computer Science, 98, 522-527. doi:10.1016/j.procs.2016.09.07010.1016/j.procs.2016.09.070Search in Google Scholar

Ermes, M., Parkka, J., Mantyjarvi, J., & Korhonen, I. (2008). Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. Information Technology in Biomedicine, IEEE Transactions, 12(1), 20-26.10.1109/TITB.2007.89949618270033Search in Google Scholar

Fahrenberg, J., Foerster, F., Smeja, M., & Muller, W. (1997). Assessment of posture and motion by multichannel piezoresistive accelerometer recordings.pdf>. Psychophysiology, 34(5), 607-612.10.1111/j.1469-8986.1997.tb01747.x9299915Search in Google Scholar

Fida, B., Bernabucci, I., Bibbo, D., Conforto, S., & Schmid, M. (2015). Varying behavior of different window sizes on the classification of static and dynamic physical activities from a single accelerometer. Med Eng Phys, 37(7), 705-711. doi:10.1016/j.medengphy.2015.04.00510.1016/j.medengphy.2015.04.00525983067Search in Google Scholar

Foina, A. G., Badia, R. M., El-Deeb, A., & Ramirez-Fernandez, F. J. (2010). Player TrackerA Tool to Analyze Sport Players using RFID. Paper presented at the 8th IEEE International Conference on Pervasive Computing and Communications Workshops, Manneheim.Search in Google Scholar

Fox, A., Spittle, M., Otago, L., & Saunders, N. (2013). Activity profiles of the Australian female netball team players during international competition: implications for training practice. J Sports Sci, 31(14), 1588-1595. doi:10.1080/02640414.2013.79294310.1080/02640414.2013.79294323672529Search in Google Scholar

Gabbett, T. (2013). Quantifying the Physical Demands of Collision Sports: Does Microsensor Technology Measure what It Clains To Measure. Journal of Strength and Conditioning Research, 27(8), 2319-2322.10.1519/JSC.0b013e318277fd2123090320Search in Google Scholar

Gastin, P. B., McLean, O. C., Breed, R. V., & Spittle, M. (2014). Tackle and impact detection in elite Australian football using wearable microsensor technology. J Sports Sci, 32(10), 947-953. doi:10.1080/02640414.2013.86892010.1080/02640414.2013.86892024499311Search in Google Scholar

Hsu, Y.-L., Yang, S.-C., Chang, H.-C., & Lai, H.-C. (2018). Human Daily and Sport Activity Recognition Using a Wearable Inertial Sensor Network. IEEE Access, 6, 31715-31728. doi:10.1109/access.2018.283976610.1109/ACCESS.2018.2839766Search in Google Scholar

Jaitner, T., & Gawin, W. (2010). A mobile measure device for the analysis of highly dynamic movement techniques. Procedia Engineering, 2(2), 3005-3010. doi:10.1016/j.proeng.2010.04.10210.1016/j.proeng.2010.04.102Search in Google Scholar

Jennings, D., Cormack, S., Coutts, A., Boyd, L., & Aughey, R., J. (2010). The validity and reliability of GPS units for measuring distance in team sport specific running patterns. International Journal of Sports Physiology and Performance, 5, 328-341.10.1123/ijspp.5.3.32820861523Search in Google Scholar

Karantonis, D. M., Narayanan, M. R., Mathie, M., Lovell, N. H., & Celler, B. G. (2006). Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans Inf Technol Biomed, 10(1), 156-167. doi:10.1109/titb.2005.85686410.1109/TITB.2005.85686416445260Search in Google Scholar

Kelly, D., Coughlan, G. F., Green, B. S., & Caulfield, B. (2012). Automatic detection of collisions in elite level rugby union using a wearable sensing device. Sports Engineering, 15(2), 81-92. doi:10.1007/s12283-012-0088-510.1007/s12283-012-0088-5Search in Google Scholar

Khan, M., Ahamed, S. I., Rahman, M., & Smith, R. (2011). A Feature Extraction Method for Realtime Human Activity Recognition on Cell Phones. Mathematics, Statistics and Computer Science Faculty Research and Publications, 183.Search in Google Scholar

Lau, H. Y., Tong, K. Y., & Zhu, H. (2009). Support vector machine for classification of walking conditions of persons after stroke with dropped foot. Hum Mov Sci, 28(4), 504-514. doi:10.1016/j.humov.2008.12.00310.1016/j.humov.2008.12.00319428134Search in Google Scholar

Lee, J. B., Mellifont, R. B., Burkett, B. J., & James, D. A. (2013). Detection of illegal race walking: a tool to assist coaching and judging. Sensors (Basel), 13(12), 16065-16074. doi:10.3390/s13121606510.3390/s131216065389284424287531Search in Google Scholar

Lee, J. B., Mellifont, R. B., James, D. A., & Burkett, B. J. (2007). The Use of Micro-Electro-Mechanical-Systems Technology to Assess Gait Characteristics. In The Impact of Technology on Sport II.10.1201/9781439828427.ch25Search in Google Scholar

Leutheuser, H., Schuldhaus, D., & Eskofier, B. M. (2013). Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset. PLoS One, 8(10), e75196. doi:10.1371/journal.pone.007519610.1371/journal.pone.0075196379399224130686Search in Google Scholar

Long, X., Yin, B., & Aarts, R. (2009). Single-accelerometer-based daily physical activity classifications. Paper presented at the Annual Internation Conference of IEEE Engineering in Medicine and Biology Society, Minneapolis, MN.10.1109/IEMBS.2009.533492519965261Search in Google Scholar

Luteberget, L. S., Spencer, M., & Gilgien, M. (2018). Validity of the Catapult ClearSky T6 Local Positioning System for Team Sports Specific Drills, in Indoor Conditions. Front Physiol, 9, 115. doi:10.3389/fphys.2018.0011510.3389/fphys.2018.00115589372329670530Search in Google Scholar

Mathie, M., Coster, A., Lovell, N. H., & Celler, B. G. (2003). Detection of daily physical activities using a triaxial accelerometer. Medical and Biological Engineering and Computing, 41(3), 296-301.10.1007/BF0234843412803294Search in Google Scholar

McNamara, D. J., Gabbett, T. J., Chapman, P., Naughton, G., & Farhart, P. (2015). The validity of microsensors to automatically detect bowling events and counts in cricket fast bowlers. Int J Sports Physiol Perform, 10(1), 71-75. doi:10.1123/ijspp.2014-006210.1123/ijspp.2014-006224911322Search in Google Scholar

Meyhew, S. R., & Wenger, H. A. (1985). Time-motion analysis in professional soccer. Journal of Human Movement Studies, 11, 49-52.Search in Google Scholar

Mitchell, E., Monaghan, D., & O’Connor, N. E. (2013). Classification of sporting activities using smartphone accelerometers. Sensors (Basel), 13(4), 5317-5337. doi:10.3390/s13040531710.3390/s130405317367313923604031Search in Google Scholar

Moeslund, T., Thomas, G., & Hilton, A. (2017). Computer vision in sports. Cham, Switzerland: Springer.Search in Google Scholar

Moran, K., Ahmadi, A., Richter, C., Mitchell, E., Kavanagh, J., & O’Connor, N. (2015). Automatic Detection, Extraction, and Analysis of Landing During a Training Session, Using a Wearable Sensor System. Procedia Engineering, 112, 184-189. doi:10.1016/j.proeng.2015.07.19710.1016/j.proeng.2015.07.197Search in Google Scholar

Neville, J., Wixted, A., Rowlands, D., & James, D. (2010). Accelerometers: An underutilized resource in sports monitoring. Paper presented at the 2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing.10.1109/ISSNIP.2010.5706766Search in Google Scholar

Nguyen, L., Rodriguez-Martin, D., Catala, A., Perez-Lopez, C., Sma, A., & Cavallaro, A. (2015). Basketball Activity Recognition using Wearable Inertial Measurement Units. Paper presented at the International Conference on Human Computer Interaction (Interaccion 15).10.1145/2829875.2829930Search in Google Scholar

Ofstad, A., Emmett, N., Szcodronski, R., & Choudhury, R. (2008). AAMPL Accelerometer Augmented Mobile Phone Localization. In Proc. the 1st AGM International workshop on mobile localization and tracking in GPS-less environments, 13-18.Search in Google Scholar

Preece, S. J., Goulermas, J. Y., Kenney, L. P., & Howard, D. (2009). A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng, 56(3), 871-879. doi:10.1109/TBME.2008.200619010.1109/TBME.2008.200619019272902Search in Google Scholar

Randers, M. B., Mujika, I., Hewitt, A., Santisteban, J., Bischoff, R., Solano, R., . . . Mohr, M. (2010). Application of four different football match analysis systems: a comparative study. J Sports Sci, 28(2), 171-182. doi:10.1080/0264041090342852510.1080/0264041090342852520391091Search in Google Scholar

Roberts-Thomson, C. L., Lokshin, A. M., & Kuzkin, V. A. (2014). Jump detection using fuzzy logic. Paper presented at the 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES).10.1109/CIES.2014.7011841Search in Google Scholar

Sadi, F., & Klukas, R. (2012). New jump trajectory determination method using low-cost MEMS sensor fusion and augmented observations for GPS/INS integration. GPS Solutions, 17(2), 139-152. doi:10.1007/s10291-012-0267-710.1007/s10291-012-0267-7Search in Google Scholar

Schmidt, M., Rheinländer, C. C., Wille, S., Wehn, N., & Jaitner, T. (2016). IMU-based determination of fatigue during long sprint. Paper presented at the Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct - UbiComp ‘16.10.1145/2968219.2968575Search in Google Scholar

Schuldhaus, D., Zwick, C., Korger, H., Dorschky, E., Kirk, R., & Eskofier, B. M. (2015). Inertial Sensor-Based Approach for Shot/Pass Classification During a Soccer Match. Paper presented at the ACM KDD Workshop on Large Scale Sports Analytics.Search in Google Scholar

Serpiello, F. R., Hopkins, W. G., Barnes, S., Tavrou, J., Duthie, G. M., Aughey, R. J., & Ball, K. (2018). Validity of an ultra-wideband local positioning system to measure locomotion in indoor sports. J Sports Sci, 36(15), 1727-1733. doi:10.1080/02640414.2017.141186710.1080/02640414.2017.141186729192842Search in Google Scholar

Shoaib, M., Bosch, S., Incel, O. D., Scholten, H., & Havinga, P. J. (2014). Fusion of smartphone motion sensors for physical activity recognition. Sensors (Basel), 14(6), 10146-10176. doi:10.3390/s14061014610.3390/s140610146411835124919015Search in Google Scholar

Ustev, Y., Incel, O., & Ersoy, C. (2013). User, Device and Orientation Independent Human Activity Recognition on Mobile Phones: Challenges and a Proposal. UbiComp 13.10.1145/2494091.2496039Search in Google Scholar

Valenti, R. G., Dryanovski, I., & Xiao, J. (2015). Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs. Sensors (Basel), 15(8), 19302-19330. doi:10.3390/s15081930210.3390/s150819302457037226258778Search in Google Scholar

Walker, E. J., McAinch, A. J., Sweeting, A., & Aughey, R. J. (2016). Inertial sensors to estimate the energy expenditure of team-sport athletes. J Sci Med Sport, 19(2), 177-181. doi:10.1016/j.jsams.2015.01.01310.1016/j.jsams.2015.01.01325804422Search in Google Scholar

Wang, J., Chen, R., Sun, X., She, M. F. H., & Wu, Y. (2011). Recognizing Human Daily Activities From Accelerometer Signal. Procedia Engineering, 15, 1780-1786. doi:10.1016/j.proeng.2011.08.33110.1016/j.proeng.2011.08.331Search in Google Scholar

Wundersitz, D. W., Josman, C., Gupta, R., Netto, K. J., Gastin, P. B., & Robertson, S. (2015). Classification of team sport activities using a single wearable tracking device. J Biomech, 48(15), 3975-3981. doi:10.1016/j.jbiomech.2015.09.01510.1016/j.jbiomech.2015.09.01526472301Search in Google Scholar

Xing, J., Ai, H., Liu, L., & Lao, S. (2011). Multiple player tracking in sports video: a dual-mode two-way bayesian inference approach with progressive observation modeling. IEEE Trans Image Process, 20(6), 1652-1667. doi:10.1109/TIP.2010.210204510.1109/TIP.2010.210204521189238Search in Google Scholar

Zhang, K., Werner, P., Sun, M., Pi-Sunyer, X., & Boozer, N. (2002). Measurmnt of human daily physical activity. Obesity Research, 11(1), 33-40.10.1038/oby.2003.712529483Search in Google Scholar