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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McGrath MJ, Scanaill CN (2013) Wellness, fitness, and lifestyle sensing applications. In Sensor technologies, pp. 217–248. Apress, Berkeley, CA
The Xsens wearable motion capture solutions, https://www.xsens.com/products/xsens-mvn/. Accessed 8 Sept 2017
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
Vračar L, Milovančević M, Karanikić P (2015) Application of smart mobile phones in vibration monitoring. Facta Universitatis, Series: Mechanical Engineering 13(2):143–153
Bilodeau EA, Bilodeau IM, Alluisi EA (1969) Principles of skill acquisition. Academic Press, New York
Lightman K (2016) Silicon gets sporty. IEEE Spectr 53(3):48–53
Bassi A, Bauer M, Fiedler M, Kramp T, van Kranenburg R, Lange S, Meissner S (2013) Enabling things to talk. Springer Berlin Heidelberg, Berlin
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
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
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
Motion capture system. Available online. http://www.qualisys.com. Accessed 31 Aug 2017
FlexiForce force sensors. https://www.tekscan.com/product-group/embedded-sensing/force-sensors. Accessed 31 Aug 2017
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
Shimmer3 IMU Unit. http://www.shimmersensing.com/products/shimmer3. Accessed 31 Aug 2017
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
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.
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
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
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)
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)
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
Si YW, Yin J (2013) OBST-based segmentation approach to financial time series. Eng Appl Artif Intell 26(26):2581–2596
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).
Kwapisz JR, Weiss GM, Moore SA (2010) Activity recognition using cell phone accelerometers. ACM Sigkdd Explor Newsl 12(2):74–82
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
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)
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
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
Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. In KDD workshop 10(16):359–370
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
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
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
Safavian SR, Landgrebe D (2013) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674
Adankon MM, Cheriet M (2004) Support vector machine. Comput Sci 1(4):1–28
Bauer BE, Kohavi R (2014) An empirical comparison of voting classi cation algorithms: bagging, boosting, and variants. Mach Learn 36(1–2):105–139
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
Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26(1):43–49
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
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
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
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
Tiger Woods. Maintain a quiet head. http://www.golfdigest.com/golf-instruction/2009-10/tiger_woods_keep_quiet_head. Accessed 10 Mar 2019
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
The authors acknowledge the financial support from the Slovenian Research Agency (research core funding No. P2-0246).
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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). https://doi.org/10.1007/s00779-019-01274-5
- Motor learning
- Sport activity signal acquisition
- Biofeedback system
- Sport data analysis
- Machine learning in sport
- Smart sport equipment