Skip to main content

FootbSense: Soccer Moves Identification Using a Single IMU

  • Conference paper
  • First Online:
Sensor- and Video-Based Activity and Behavior Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 291))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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)

    Google Scholar 

  2. Das Antar, A., Ahmed, M., Ahad, M.A.R.: Sensor-Based Human Activity and Behavior Computing, pp. 147–176. Springer International Publishing, Cham (2021)

    Google Scholar 

  3. 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)

    Google Scholar 

  4. 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)

    Google Scholar 

  5. 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)

    Google Scholar 

  6. 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)

    Google Scholar 

  7. 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)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. 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)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. 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)

    Google Scholar 

  12. 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)

    Google Scholar 

  13. 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

  14. 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)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. Bojanova, I.: It enhances football at world cup 2014. IT Prof. 16(4), 12–17 (2014)

    Article  Google Scholar 

  17. Metulini, R.: Players movements and team shooting performance: a data mining approach for basketball (2018). arXiv preprint arXiv:1805.02501

  18. 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)

    Google Scholar 

  19. 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)

    Google Scholar 

  20. 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)

    Google Scholar 

  21. Merton McGinnis, P.: Biomechanics of Sport and Exercise. Human Kinetics (2013)

    Google Scholar 

  22. 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)

    Google Scholar 

  23. Poppe, R.: A survey on vision-based human action recognition. Image Vis. Comput. 28(6), 976–990 (2010)

    Article  Google Scholar 

  24. 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)

    Google Scholar 

  25. 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)

    Google Scholar 

  26. Li, Y., Peng, X., Zhou, G., Zhao, H.: Smartjump: a continuous jump detection framework on smartphones. IEEE Internet Comput. 24(2), 18–26 (2020)

    Google Scholar 

  27. 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)

    Google Scholar 

  28. Weiss, G.M., Yoneda, K., Hayajneh, T.: Smartphone and smartwatch-based biometrics using activities of daily living. IEEE Access 7, 133190–133202 (2019)

    Google Scholar 

  29. 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)

    Google Scholar 

  30. 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)

    Google Scholar 

  31. Ishii, S., Yokokubo, A., Luimula, M., Lopez, G.: ExerSense: physical exercise recognition and counting algorithm from wearables robust to positioning. Sensors 21(1) (2021)

    Google Scholar 

  32. 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)

    Google Scholar 

  33. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3) (2011)

    Google Scholar 

  34. Deng, H., Runger, G., Tuv, E., Vladimir, M.: A time series forest for classification and feature extraction. Inf. Sci. 239, 142–153 (2013)

    Google Scholar 

  35. 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)

    Google Scholar 

  36. Schäfer, P.: The boss is concerned with time series classification in the presence of noise. Data Min. Knowl. Discov. 29(6), 1505–1530 (2015)

    Article  MathSciNet  Google Scholar 

  37. Alobaid, O., Ramaswamy, L.: A feature-based approach for identifying soccer moves using an accelerometer sensor. In: HEALTHINF, pp. 34–44 (2020)

    Google Scholar 

  38. 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)

    Google Scholar 

  39. Movesense: https://www.movesense.com/. Accessed on 26 July 2021

  40. Motorola: https://www.motorola.com/us/. Accessed on 14 Jan 2021

Download references

Acknowledgements

This work was supported by Aoyama Gakuin University Research Institute grant program for creation of innovative research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shun Ishii .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

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

Publish with us

Policies and ethics