Advertisement

The role of technology for accelerated motor learning in sport

  • Matevž Pustišek
  • Yu WeiEmail author
  • Yunchuan Sun
  • Anton Umek
  • Anton Kos
Original Article

Abstract

Wearable devices measuring some physical or physiological quantity of an individual have already become a part of daily life for many people. While such simple devices output mainly the statistical values of measured quantities or count events, demands in sport are more stringent. Quantities of interest must be measured in a wider range, with a greater precision, and with a higher sampling frequency. We present a short introduction to motor learning in sport and its needs for technology back-up. We present properties and limitations of various sensors used for sport activity signal acquisition, means of communication, and properties and limitations of communication channels. We shed some light on the analysis of various aspects of sport activity signal and data processing. We present timing, spatial, and computational power constraints of processing. Attention is given also to the state of the art data processing techniques such as machine learning and data mining. We also put into context some technological trends and challenges in sport, such as the Internet of Things, fog and cloud computing, smart sport equipment, and real-time biofeedback systems and applications.

Keywords

Motor learning Sensors Sport activity signal acquisition Biofeedback system Sport data analysis Machine learning in sport Smart sport equipment 

Notes

Acknowledgments

The authors acknowledge the financial support from the Slovenian Research Agency (research core funding No. P2-0246).

References

  1. 1.
    McGrath MJ, Scanaill CN (2013) Wellness, fitness, and lifestyle sensing applications. In Sensor technologies, pp. 217–248. Apress, Berkeley, CAGoogle Scholar
  2. 2.
    The Xsens wearable motion capture solutions, https://www.xsens.com/products/xsens-mvn/. Accessed 8 Sept 2017
  3. 3.
    Grün T, Franke N, Wolf D, Witt N, Eidloth A (2011) A Real-Time Tracking System for Football Match and Training Analysis. In: Heuberger A, Elst G, Hanke R (eds) Microelectronic Systems. Springer, Berlin, HeidelbergGoogle Scholar
  4. 4.
    Vračar L, Milovančević M, Karanikić P (2015) Application of smart mobile phones in vibration monitoring. Facta Universitatis, Series: Mechanical Engineering 13(2):143–153Google Scholar
  5. 5.
    Bilodeau EA, Bilodeau IM, Alluisi EA (1969) Principles of skill acquisition. Academic Press, New YorkGoogle Scholar
  6. 6.
    Lightman K (2016) Silicon gets sporty. IEEE Spectr 53(3):48–53CrossRefGoogle Scholar
  7. 7.
    Bassi A, Bauer M, Fiedler M, Kramp T, van Kranenburg R, Lange S, Meissner S (2013) Enabling things to talk. Springer Berlin Heidelberg, BerlinCrossRefGoogle Scholar
  8. 8.
    Bonomi F, Milito R, Natarajan P, Zhu J (2014) Fog computing: a platform for Internet of Things and analytics. In: Bessis N, Dobre C (Eds) Big data and Internet of Things: A Roadmap for Smart Environments. Studies in Computational Intelligence, vol 546. Springer, ChamGoogle Scholar
  9. 9.
    Liebermann DG, Katz L, Hughes MD, Bartlett RM, McClements J, Franks IM (2002) Advances in the application of information technology to sport performance. J Sports Sci 20:755–769CrossRefGoogle Scholar
  10. 10.
    Sigrist R, Rauter G, Riener R, Wolf P (2013) Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review. Psychon Bull Rev 20:21–53.  https://doi.org/10.3758/s13423-012-0333-8 CrossRefGoogle Scholar
  11. 11.
    Motion capture system. Available online. http://www.qualisys.com. Accessed 31 Aug 2017
  12. 12.
    FlexiForce force sensors. https://www.tekscan.com/product-group/embedded-sensing/force-sensors. Accessed 31 Aug 2017
  13. 13.
    Đorđević S, Stančin S, Meglič A, Milutinović V, Tomažič S (2011) Mc sensor—a novel method for measurement of muscle tension. Sensors 11(10):9411–9425CrossRefGoogle Scholar
  14. 14.
    Shimmer3 IMU Unit. http://www.shimmersensing.com/products/shimmer3. Accessed 31 Aug 2017
  15. 15.
    Cavallari R, Martelli F, Rosini R, Buratti C, Verdone R (2014) A survey on wireless body area networks: technologies and design challenges. IEEE Communications Surveys & Tutorials 16(3):1635–1657CrossRefGoogle Scholar
  16. 16.
    Yi BK, Faloutsos C (2000) Fast time sequence indexing for arbitrary Lp norms, in Editor (Ed.) Fast time sequence indexing for arbitrary Lp norms. In VLDB 385(394):99.Google Scholar
  17. 17.
    Keogh E, Chakrabarti K, Pazzani M, Mehrotra S (2001) Dimensionality reduction for fast similarity search in large time series databases. Knowledge & Information Systems 3(3):263–286CrossRefzbMATHGoogle Scholar
  18. 18.
    Smyth P, Keogh E (1997) Clustering and mode classification of engineering time series data. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 24–30Google Scholar
  19. 19.
    Keogh EJ, Pazzani MJ, Keogh EJ (1998) An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. In Kdd 98:239–243)Google Scholar
  20. 20.
    Chung FL, Fu TC, Luk R, Ng V (2001) Flexible time series pattern matching based on perceptually important points, Workshop on Learning from Temporal and Spatial Data in International Joint Conference on Artificial Intelligence (IJCAI'01), Seattle, Washington, USA, (pp. 1–7)Google Scholar
  21. 21.
    Chung FL, Fu TC, Ng V, Luk RWP (2004) An evolutionary approach to pattern-based time series segmentation. IEEE T Evolut Comput 8(5):471–489CrossRefGoogle Scholar
  22. 22.
    Si YW, Yin J (2013) OBST-based segmentation approach to financial time series. Eng Appl Artif Intell 26(26):2581–2596CrossRefGoogle Scholar
  23. 23.
    Yin J, Si YW, Gong Z (2011) Financial time series segmentation based on Turning Points. In Proceedings 2011 International Conference on System Science and Engineering (pp. 394-399).Google Scholar
  24. 24.
    Kwapisz JR, Weiss GM, Moore SA (2010) Activity recognition using cell phone accelerometers. ACM Sigkdd Explor Newsl 12(2):74–82CrossRefGoogle Scholar
  25. 25.
    Yurtman A, Barshan B (2014) Automated evaluation of physical therapy exercises using multi-template dynamic time warping on wearable sensor signals. Comput Methods Prog Biomed 117(2):189–207CrossRefGoogle Scholar
  26. 26.
    Siirtola P, Laurinen P, Ning J (2009) Mining an optimal prototype from a periodic time series: an evolutionary computation-based approach. In 2009 IEEE Congress on Evolutionary Computation (pp. 2818–2824)Google Scholar
  27. 27.
    Raghavendra BS, Narayanan CK, Sita G, Ramakrishnan AG, Sriganesh M (2005) Prototype learning methods for online handwriting recognition. In Eighth International Conference on Document Analysis and Recognition (ICDAR'05) pp. 287–291Google Scholar
  28. 28.
    Sakoe H, Chiba S (1971) A dynamic programming approach to continuous speech recognition. In Proceedings of the Seventh International Congress on Acoustics, Budapest, 3, page 65–69. Budapest, Akadémiai KiadóGoogle Scholar
  29. 29.
    Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. In KDD workshop 10(16):359–370Google Scholar
  30. 30.
    Lin J, Keogh E, Lonardi S, Chiu B (2003) A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery (pp. 2–11). ACMGoogle Scholar
  31. 31.
    Ye L, Keogh E (2009) Time series shapelets: a new primitive for data mining. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 947–956). ACMGoogle Scholar
  32. 32.
    Lee S, Shimoji S (1993) BAYESNET: Bayesian classification network based on biased random competition using Gaussian kernels. In Neural Networks, 1993., IEEE International Conference on (pp. 1354–1359). IEEEGoogle Scholar
  33. 33.
    Safavian SR, Landgrebe D (2013) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674Google Scholar
  34. 34.
    Adankon MM, Cheriet M (2004) Support vector machine. Comput Sci 1(4):1–28zbMATHGoogle Scholar
  35. 35.
    Bauer BE, Kohavi R (2014) An empirical comparison of voting classi cation algorithms: bagging, boosting, and variants. Mach Learn 36(1–2):105–139Google Scholar
  36. 36.
    Huang F, Xie G, Xiao R (2009) Research on ensemble learning. In 2009 International Conference on Artificial Intelligence and Computational Intelligence 3:249–252). IEEEGoogle Scholar
  37. 37.
    Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26(1):43–49CrossRefzbMATHGoogle Scholar
  38. 38.
    Lu D, Guo J, Zhou X, Zhao G, Bie R (2016) Self-learning based motion recognition using sensors embedded in a smartphone for mobile healthcare. In Wireless algorithms, systems, and applications: 11th International Conference, WASA, Bozeman, MT, USA, August 8–10, 2016. Proceedings, vol. 9798, p. 343. Springer, 2016Google Scholar
  39. 39.
    Guo J, Zhou X, Sun Y, Ping G, Zhao G, Li Z (2016) Smartphone-based patients’ activity recognition by using a self-learning scheme for medical monitoring. J Med Syst 40(6):140CrossRefGoogle Scholar
  40. 40.
    Wei Y, Jiao L, Wang S, Bie R, Chen Y, Liu D (2016) Sports motion recognition using MCMR features based on interclass symbolic distance. Int J Distrib Sens Netw 12(5):7483536CrossRefGoogle Scholar
  41. 41.
    Umek A, Tomažič S, Kos A (2015) Wearable training system with real-time biofeedback and gesture user interface. Pers Ubiquit Comput 19(7):989–998CrossRefGoogle Scholar
  42. 42.
    Tiger Woods. Maintain a quiet head. http://www.golfdigest.com/golf-instruction/2009-10/tiger_woods_keep_quiet_head. Accessed 10 Mar 2019
  43. 43.
    Bob Doyle Experts weigh in on head movement during the golf swing. https://foreverbettergolf.com/articles/experts-weigh-in-on-head-movement-during-the-golf-swing/. Accessed 10 Mar 2019

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Matevž Pustišek
    • 1
  • Yu Wei
    • 2
    Email author
  • Yunchuan Sun
    • 3
  • Anton Umek
    • 1
  • Anton Kos
    • 1
  1. 1.Faculty of Electrical EngineeringUniversity of LjubljanaLjubljanaSlovenia
  2. 2.Computer Teaching and Research SectionCapital University of Physical Education and SportsBeijingChina
  3. 3.Business schoolBeijing Normal UniversityBeijingChina

Personalised recommendations