Abstract
Although wearable technologies are commonly used for sports at elite levels, these systems are expensive, and it is still difficult to recognize detailed player movements. We introduce a soccer movements recognition system using a single wearable sensor to aid the skill improvement for amateur players. We collected 3-axis acceleration data of six soccer movements and validated the proposing system. We also compared three sensor locations to find the best accurate location. With ensemble bagged trees classification method, we achieved 78.7% classification accuracy of six basic soccer movements from the inside-ankle sensor. Moreover, our results show that it is possible to distinguish between running and dribbling, passing and shooting, even though they are similar movements in soccer. Besides, the second highest accuracy was achieved from a sensor placed on the upper part of the back, which is a safer wearing position compared to other locations. These results suggest that our approach enables a new category of wearable recognition system for amateur soccer.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Skawinski, K., Montraveta Roca, F., Dieter Findling, R., Sigg, S.: Workout type recognition and repetition counting with CNNs from 3D acceleration sensed on the chest. In: International Work-Conference on Artificial Neural Networks, pp. 347–359. Springer, Berlin (2019)
Das Antar, A., Ahmed, M., Ahad, M.A.R.: Sensor-Based Human Activity and Behavior Computing, pp. 147–176. Springer International Publishing, Cham (2021)
Hossain, T., Islam, Md.S., Ahad, M.A.R., Inoue, S.: Human activity recognition using earable device. In: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, UbiComp/ISWC’19 Adjunct, pp. 81–84. Association for Computing Machinery, New York, NY, USA (2019)
Das Antar, A., Ahmed, M., Ahad, M.A.R.: Challenges in sensor-based human activity recognition and a comparative analysis of benchmark datasets: a review. In: 2019 Joint 8th International Conference on Informatics, Electronics Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision Pattern Recognition (icIVPR), pp. 134–139 (2019)
Inoue, S., Lago, P., Hossain, T., Mairittha, T., Mairittha, N.: Integrating activity recognition and nursing care records: the system, deployment, and a verification study. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(3) (2019)
Manjarres, J., Narvaez, P., Gasser, K., Percybrooks, W., Pardo, M.: Physical workload tracking using human activity recognition with wearable devices. Sensors 20(1), 39 (2020)
Ahad, M.A.R., Das Antar, A., Shahid, O.: Vision-based action understanding for assistive healthcare: a short review. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2019)
Ahad, M.A.R., Ahmed, M., Das Antar, A., Makihara, Y., Yagi, Y.: Action recognition using kinematics posture feature on 3d skeleton joint locations. Pattern Recogn. Lett. 145, 216–224 (2021)
Tong, C., Tailor, S.A., Lane, N.D.: Are accelerometers for activity recognition a dead-end? In: Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications, pp. 39–44 (2020)
Zhang, S., Wei, Z., Nie, J., Huang, L., Wang, S., Li, Z.: A review on human activity recognition using vision-based method. J. Healthcare Eng. 2017 (2017)
Malawski, F., Kwolek, B.: Classification of basic footwork in fencing using accelerometer. In: 2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pp. 51–55. IEEE (2016)
Luis Felipe, J., Garcia-Unanue, J., Viejo-Romero, D., Navandar, A., Sánchez-Sánchez, J.: Validation of a video-based performance analysis system (mediacoach®) to analyze the physical demands during matches in LaLiga. Sensors 19(19), 4113 (2019)
Sap and the German football association turn big data into smart decisions to improve player performance at the world cup in Brazil. https://news.sap.com/2014/06/sap-dfb-turn-big-data-smart-data-world-cup-brazil/. Accessed on 26 July 2021
Kim, W., Kim, M.: Sports motion analysis system using wearable sensors and video cameras. In: 2017 International Conference on Information and Communication Technology Convergence (ICTC), pp. 1089–1091. IEEE (2017)
Chmura, P., Andrzejewski, M., Konefał, M., Mroczek, D., Rokita, A., Chmura, J.: Analysis of motor activities of professional soccer players during the 2014 world cup in Brazil. J. Human Kinet. 56(1), 187–195 (2017)
Bojanova, I.: It enhances football at world cup 2014. IT Prof. 16(4), 12–17 (2014)
Metulini, R.: Players movements and team shooting performance: a data mining approach for basketball (2018). arXiv preprint arXiv:1805.02501
Taylor, J.B., Wright, A.A., Dischiavi, S.L., Townsend, M.A., Marmon, A.R.: Activity demands during multi-directional team sports: a systematic review. Sports Med. 47(12), 2533–2551 (2017)
Taghavi, S., Davari, F., Tabatabaee Malazi, H., Ali Abin, A.: Tennis stroke detection using inertial data of a smartwatch. In: 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 466–474. IEEE (2019)
Pons, E., García-Calvo, T., Resta, R., Blanco, H., del Campo, R.L., Díaz García, J., José Pulido, J.: A comparison of a GPS device and a multi-camera video technology during official soccer matches: agreement between systems. Plos One 14(8), e0220729 (2019)
Merton McGinnis, P.: Biomechanics of Sport and Exercise. Human Kinetics (2013)
Fullerton, E., Heller, B., Munoz-Organero, M.: Recognizing human activity in free-living using multiple body-worn accelerometers. IEEE Sens. J. 17(16), 5290–5297 (2017)
Poppe, R.: A survey on vision-based human action recognition. Image Vis. Comput. 28(6), 976–990 (2010)
Ahmed, M., Das Antar, A., Ahad, M.A.R.: An approach to classify human activities in real-time from smartphone sensor data. In: 2019 Joint 8th International Conference on Informatics, Electronics Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision Pattern Recognition (icIVPR), pp. 140–145 (2019)
Sayan Saha, S., Rahman, S., Ridita Haque, Z.R., Hossain, T., Inoue, S., Ahad, M.A.R.: Position independent activity recognition using shallow neural architecture and empirical modeling. In: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, UbiComp/ISWC’19 Adjunct, pp. 808–813. Association for Computing Machinery, New York, NY, USA (2019)
Li, Y., Peng, X., Zhou, G., Zhao, H.: Smartjump: a continuous jump detection framework on smartphones. IEEE Internet Comput. 24(2), 18–26 (2020)
Shahmohammadi, F., Hosseini, A., King, C.E., Sarrafzadeh, M.: Smartwatch based activity recognition using active learning. In: Proceedings of the Second IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE’17, pp. 321–329. IEEE Press (2017)
Weiss, G.M., Yoneda, K., Hayajneh, T.: Smartphone and smartwatch-based biometrics using activities of daily living. IEEE Access 7, 133190–133202 (2019)
Sukreep, S., Elgazzar, K., Henry Chu, C., Nukoolkit, C., Mongkolnam, P.: Recognizing falls, daily activities, and health monitoring by smart devices. Sens. Mater. 31(6), 1847–1869 (2019)
Morris, D., Scott Saponas, T., Guillory, A., Kelner, I.: RecoFit: using a wearable sensor to find, recognize, and count repetitive exercises. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3225–3234 (2014)
Ishii, S., Yokokubo, A., Luimula, M., Lopez, G.: ExerSense: physical exercise recognition and counting algorithm from wearables robust to positioning. Sensors 21(1) (2021)
Nguyen, L.N.N., Rodríguez-Martín, D., Català, A., Pérez-López, C., Samà, A., Cavallaro, A.: Basketball activity recognition using wearable inertial measurement units. In: Proceedings of the XVI International Conference on Human Computer Interaction, pp. 1–6 (2015)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3) (2011)
Deng, H., Runger, G., Tuv, E., Vladimir, M.: A time series forest for classification and feature extraction. Inf. Sci. 239, 142–153 (2013)
Rakthanmanon, T., Keogh, E.: Fast shapelets: a scalable algorithm for discovering time series shapelets. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 668–676. SIAM (2013)
Schäfer, P.: The boss is concerned with time series classification in the presence of noise. Data Min. Knowl. Discov. 29(6), 1505–1530 (2015)
Alobaid, O., Ramaswamy, L.: A feature-based approach for identifying soccer moves using an accelerometer sensor. In: HEALTHINF, pp. 34–44 (2020)
Henriksen, A., Haugen Mikalsen, M., Zebene Woldaregay, A., Muzny, M., Hartvigsen, G., Arnesdatter Hopstock, L., Grimsgaard, S.: Using fitness trackers and smartwatches to measure physical activity in research: analysis of consumer wrist-worn wearables. J. Med. Internet Res. 20(3), e110 (2018)
Movesense: https://www.movesense.com/. Accessed on 26 July 2021
Motorola: https://www.motorola.com/us/. Accessed on 14 Jan 2021
Acknowledgements
This work was supported by Aoyama Gakuin University Research Institute grant program for creation of innovative research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kondo, Y., Ishii, S., Aoyagi, H., Hossain, T., Yokokubo, A., Lopez, G. (2022). FootbSense: Soccer Moves Identification Using a Single IMU. In: Ahad, M.A.R., Inoue, S., Roggen, D., Fujinami, K. (eds) Sensor- and Video-Based Activity and Behavior Computing. Smart Innovation, Systems and Technologies, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-19-0361-8_7
Download citation
DOI: https://doi.org/10.1007/978-981-19-0361-8_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0360-1
Online ISBN: 978-981-19-0361-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)