Artificially Intelligent Assistant for Basketball Coaching

  • Yasin Acikmese
  • Baris Can Ustundag
  • Tarik UzunovicEmail author
  • Edin Golubovic
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 83)


Technological advancements in wearable sensors, machine learning and Internet of Things (IoT) is opening new perspectives on the understanding of physiological, biomechanical, and psychological mechanisms of human movement in sport disciplines. Utilization of technology and field expertise allows the development of artificially intelligent (AI) assistants for sport coaching. This paper considers the development of AI assistant for basketball coaching. The assistant architecture and design is explained in detail. The details about the development of knowledge model for basketball exercise recognition are supplied and experimental verification of proposed method is done.


AI assistant Wearable Machine learning IoT Basketball Kinesiology Neural Networks 


  1. 1.
    Gregg, D.G.: E-learning agents. Learn. Organ. 14(4), 300–312 (2007)CrossRefGoogle Scholar
  2. 2.
    Solana, J., Caceres, C., Garcia-Molina, A., Chausa, P., Opisso, E., Roig-Rovira, T., Gomez, E.J.: Intelligent therapy assistant (ITA) for cognitive rehabilitation in patients with acquired brain injury. BMC Med. Inf. Decis. Making 14(1), 58 (2014)CrossRefGoogle Scholar
  3. 3.
    Dragone, M., Saunders, J., Dautenhahn, K.: On the integration of adaptive and interactive robotic smart spaces. Paladyn J. Behav. Rob. 6(1), 165–179 (2015) Google Scholar
  4. 4.
    Beetz, M., Jain, D., Mosenlechner, L., Tenorth, M., Kunze, L., Blodow, N., Pangercic, D.: Cognition-enabled autonomous robot control for the realization of home chore task intelligence. Proc. IEEE 100(8), 2454–2471 (2012)CrossRefGoogle Scholar
  5. 5.
    Biundo, S., Bercher, P., Geier, T., Müller, F., Schattenberg, B.: Advanced user assistance based on AI planning. Cogn. Syst. Res. 12(3–4), 219–236 (2011)CrossRefGoogle Scholar
  6. 6.
    Bercher, P., Richter, F., Hornle, T., Geier, T., Holler, D., Behnke, G., Nothdurft, F., Honold, F., Minker, W., Weber, M., Biundo, S.: A planning-based assistance system for setting up a home theater. In: Proceedings of the 29th National Conference on Artificial Intelligence (AAAI), pp. 4264–4265 (2015)Google Scholar
  7. 7.
    Vales-Alonso, J., Chaves-Dieguez, D., Lepez-Matencio, P., Alcaraz, J.J., Parrado-García, F.J., Gonzalez-Castano, F.J.: SAETA: a smart coaching assistant for professional volleyball training. IEEE Trans. Syst. Man Cybern.: Syst. 45(8), 1138–1150 (2015)CrossRefGoogle Scholar
  8. 8.
    Klein, M.C., Manzoor, A., Middelweerd, A., Mollee, J.S., te Velde, S.J.: Encouraging physical activity via a personalized mobile system. IEEE Internet Comput. 19(4), 20–27 (2015)CrossRefGoogle Scholar
  9. 9.
    Dijkhuis, T.B., Blaauw, F.J., van Ittersum, M.W., Velthuijsen, H., Aiello, M.: Personalized physical activity coaching: a machine learning approach. Sensors 18(2), 623 (2018)CrossRefGoogle Scholar
  10. 10.
    Bacic, B., Hume, P.: Computational intelligence for qualitative coaching diagnostics: automated assessment of tennis swings to improve performance and safety. arXiv preprint arXiv:1711.09562 (2017)
  11. 11.
    Ghasemzadeh, H., Loseu, V., Jafari, R.: Wearable coach for sport training: a quantitative model to evaluate wrist-rotation in golf. J. Ambient Intell. Smart Environ. 1(2), 173–184 (2009)Google Scholar
  12. 12.
    Acikmese, Y., Ustundag, B.C., Golubovic, E.: Towards an artificial training expert system for basketball. In: 10th International IEEE Conference on Electrical and Electronics Engineering (ELECO), pp. 1300–1304 (2017)Google Scholar
  13. 13.
    Yang, C.C., Hsu, Y.L.: A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 10(8), 7772–7788 (2010)CrossRefGoogle Scholar
  14. 14.
    Ren, X., Ding, W., Crouter, S.E., Mu, Y., Xie, R.: Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning. Appl. Intell. 45(2), 512–529 (2016)CrossRefGoogle Scholar
  15. 15.
    Wearable Sensor v2, Inovatink.
  16. 16.
    Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutorials 15(3), 1192–1209 (2013)CrossRefGoogle Scholar
  17. 17.
    Rioul, O., Vetterli, M.: Wavelets and signal processing. IEEE Sig. Process. Mag. 8(4), 14–38 (1991)CrossRefGoogle Scholar
  18. 18.
    Guo, L., Rivero, D., Seoane, J.A., Pazos, A.: Classification of EEG signals using relative wavelet energy and artificial neural networks. In: Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation (ACM), pp. 177–184 (2009)Google Scholar
  19. 19.
    Khan, M., Ahamed, S.I., Rahman, M., Smith, R.O.: A feature extraction method for realtime human activity recognition on cell phones. In: Proceedings of 3rd International Symposium on Quality of Life Technology (isQoLT 2011) (2011)Google Scholar
  20. 20.
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson Education Limited, Malaysia (2016)zbMATHGoogle Scholar
  21. 21.
    Kohavi, R.: A study of cross validation and bootstrap for accuracy estimation and model selection. In: International Joint Conference on Artificial Intelligence (IJCAI) (1995)Google Scholar
  22. 22.
    Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Industrial EngineeringGalatasaray UniversityIstanbulTurkey
  2. 2.Department of Computer EngineeringIstanbul Technical UniversityIstanbulTurkey
  3. 3.Faculty of Electrical EngineeringUniversity of SarajevoSarajevoBosnia and Herzegovina
  4. 4.InovatinkIstanbulTurkey

Personalised recommendations