The role of technology for accelerated motor learning in sport


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.

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  1. 1.

    McGrath MJ, Scanaill CN (2013) Wellness, fitness, and lifestyle sensing applications. In Sensor technologies, pp. 217–248. Apress, Berkeley, CA

  2. 2.

    The Xsens wearable motion capture solutions, 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, Heidelberg

  4. 4.

    Vračar L, Milovančević M, Karanikić P (2015) Application of smart mobile phones in vibration monitoring. Facta Universitatis, Series: Mechanical Engineering 13(2):143–153

  5. 5.

    Bilodeau EA, Bilodeau IM, Alluisi EA (1969) Principles of skill acquisition. Academic Press, New York

  6. 6.

    Lightman K (2016) Silicon gets sporty. IEEE Spectr 53(3):48–53

    Article  Google 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, Berlin

    Google 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, Cham

  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 20:755–769

    Article  Google 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.

    Article  Google Scholar 

  11. 11.

    Motion capture system. Available online. Accessed 31 Aug 2017

  12. 12.

    FlexiForce 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):9411–9425

    Article  Google Scholar 

  14. 14.

    Shimmer3 IMU Unit. 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):1635–1657

    Article  Google 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.

  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):263–286

    Article  MATH  Google 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. 24–30

  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)

  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)

  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):471–489

    Article  Google Scholar 

  22. 22.

    Si YW, Yin J (2013) OBST-based segmentation approach to financial time series. Eng Appl Artif Intell 26(26):2581–2596

    Article  Google 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).

  24. 24.

    Kwapisz JR, Weiss GM, Moore SA (2010) Activity recognition using cell phone accelerometers. ACM Sigkdd Explor Newsl 12(2):74–82

    Article  Google 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):189–207

    Article  Google 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)

  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. 287–291

  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 Kiadó

  29. 29.

    Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. In KDD workshop 10(16):359–370

  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). ACM

  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). ACM

  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). IEEE

  33. 33.

    Safavian SR, Landgrebe D (2013) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674

  34. 34.

    Adankon MM, Cheriet M (2004) Support vector machine. Comput Sci 1(4):1–28

    MATH  Google 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):105–139

    Google 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). IEEE

  37. 37.

    Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26(1):43–49

    Article  MATH  Google 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, 2016

  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):140

    Article  Google 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):7483536

    Article  Google 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):989–998

    Article  Google Scholar 

  42. 42.

    Tiger Woods. Maintain a quiet head. Accessed 10 Mar 2019

  43. 43.

    Bob Doyle Experts weigh in on head movement during the golf swing. Accessed 10 Mar 2019

Download references


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

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Correspondence to Yu Wei.

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Pustišek, M., Wei, Y., Sun, Y. et al. The role of technology for accelerated motor learning in sport. Pers Ubiquit Comput (2019).

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