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Sports Medicine

, Volume 45, Issue 7, pp 1065–1081 | Cite as

The Use of Wearable Microsensors to Quantify Sport-Specific Movements

  • Ryan Chambers
  • Tim J. Gabbett
  • Michael H. Cole
  • Adam Beard
Systematic Review

Abstract

Background

Microtechnology has allowed sport scientists to understand the locomotor demands of various sports. While wearable global positioning technology has been used to quantify the locomotor demands of sporting activities, microsensors (i.e. accelerometers, gyroscopes and magnetometers) embedded within the units also have the capability to detect sport-specific movements.

Objective

The objective of this study was to determine the extent to which microsensors (also referred to as inertial measurement units and microelectromechanical sensors) have been utilised in quantifying sport-specific movements.

Methods

A systematic review of the use of microsensors and associated terms to evaluate sport-specific movements was conducted; permutations of the terms used included alternate names of the various technologies used, their applications and different applied environments. Studies for this review were published between 2008 and 2014 and were identified through a systematic search of six electronic databases: Academic Search Complete, CINAHL, PsycINFO, PubMed, SPORTDiscus, and Web of Science. Articles were required to have used athlete-mounted sensors to detect sport-specific movements (e.g. rugby union tackle) rather than sensors mounted to equipment and monitoring generic movement patterns.

Results

A total of 2395 studies were initially retrieved from the six databases and 737 results were removed as they were duplicates, review articles or conference abstracts. After screening titles and abstracts of the remaining papers, the full text of 47 papers was reviewed, resulting in the inclusion of 28 articles that met the set criteria around the application of microsensors for detecting sport-specific movements. Eight articles addressed the use of microsensors within individual sports, team sports provided seven results, water sports provided eight articles, and five articles addressed the use of microsensors in snow sports. All articles provided evidence of the ability of microsensors to detect sport-specific movements. Results demonstrated varying purposes for the use of microsensors, encompassing the detection of movement and movement frequency, the identification of movement errors and the assessment of forces during collisions.

Conclusion

This systematic review has highlighted the use of microsensors to detect sport-specific movements across a wide range of individual and team sports. The ability of microsensors to capture sport-specific movements emphasises the capability of this technology to provide further detail on athlete demands and performance. However, there was mixed evidence on the ability of microsensors to quantify some movements (e.g. tackling within rugby union, rugby league and Australian rules football). Given these contrasting results, further research is required to validate the ability of wearable microsensors containing accelerometers, gyroscopes and magnetometers to detect tackles in collision sports, as well as other contact events such as the ruck, maul and scrum in rugby union.

Keywords

Global Position System Inertial Measurement Unit Team Sport Rugby League Golf Swing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

No funding was received for the conduct of this study and preparation of the manuscript. The authors have no conflicts of interest that are relevant to the content of this review.

References

  1. 1.
    Cunniffe B, Proctor W, Baker J, et al. An evaluation of the physiological demands of elite rugby union using global positioning system tracking software. J Strength Cond Res. 2009;23(4):1195–203.PubMedCrossRefGoogle Scholar
  2. 2.
    Gabbett T, Jenkins D, Abernethy B. Physical demands of professional rugby league training and competition using microtechnology. J Sci Med Sport. 2012;15(1):80–6.PubMedCrossRefGoogle Scholar
  3. 3.
    Wisbey B, Montgomery P, Pyne D, et al. Quantifying movement demands of AFL football using GPS tracking. J Sci Med Sport. 2010;13(5):531–6.PubMedCrossRefGoogle Scholar
  4. 4.
    Boyd L, Ball K, Aughey R. The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. Int J Sports Physiol Perform. 2014;6:311–21.Google Scholar
  5. 5.
    Cummins C, Orr R, O’Connor H, et al. Global positioning systems (GPS) and microtechnology sensors in team sports: a systematic review. Sports Med. 2013;43(10):1025–42.PubMedCrossRefGoogle Scholar
  6. 6.
    Gabbett T, Whyte D, Hartwig T, et al. The relationship between workloads, physical performance, injury and illness in adolescent male football players. Sports Med. 2014;44(7):989–1003.PubMedCrossRefGoogle Scholar
  7. 7.
    Gabbett T. Quantifying the physical demands of collision sports: does microsensor technology measure what it claims to measure? J Strength Cond Res. 2013;27(8):2319–22.PubMedCrossRefGoogle Scholar
  8. 8.
    Montgomery P, Pyne D, Minahan C. The physical and physiological demands of basketball training and competition. Int J Sports Physiol Perform. 2010;5:75–86.PubMedGoogle Scholar
  9. 9.
    Cormack S, Smith R, Mooney M, et al. Accelerometer load as a measure of activity profile in different standards of netball match play. Int J Sports Physiol Perform. 2014;9:283–91.PubMedCrossRefGoogle Scholar
  10. 10.
    Chandler P, Pinder S, Curran J, et al. Physical demands of training and competition in collegiate netball players. J Strength Cond Res. 2014;28(10):2732–7.PubMedCrossRefGoogle Scholar
  11. 11.
    Lee J, Mellifont R, Burkett B. The use of a single inertial sensor to identify stride, step, and stance durations of running gait. J Sci Med Sport. 2010;13(2):270–3.PubMedCrossRefGoogle Scholar
  12. 12.
    Plasqui G, Westerterp K. Physical activity assessment with accelerometers: an evaluation against doubly labelled water. Obesity. 2007;15(10):2371–9.PubMedCrossRefGoogle Scholar
  13. 13.
    Jean-Louis G, Von Gizycki H, Zizi F, et al. Determination of sleep and wakefulness with the actigraph data analysis software (ADAS). Sleep. 1996;19(9):739–43.PubMedGoogle Scholar
  14. 14.
    Weaving D, Marshall P, Earle K, et al. A combination of internal and external training load measures explains the greatest proportion of variance in certain training modes in professional rugby league. Int J Sports Physiol Perform. 2014;9:905–12.PubMedCrossRefGoogle Scholar
  15. 15.
    Bonomi A, Goris A, Yin B, et al. Detection of type, duration, and intensity of physical activity using an accelerometer. Med Sci Sports Exerc. 2009;41(9):1770–7.PubMedCrossRefGoogle Scholar
  16. 16.
    Choukou M, Laffaye G, Taiar R. Reliability and validity of an accelerometric system for assessing vertical jumping performance. Biol Sport. 2014;31(1):55–62.PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Adelsberger R, Troster G. Experts lift differently: classification of weight-lifting athletes. In: IEEE conference on body sensor networks (BSN). Cambridge, MA; 2013. p. 1–6.Google Scholar
  18. 18.
    Ahmadi A, Rowlands D, James D. Towards a wearable device for skill assessment and skill acquisition of a tennis player during the first serve. Sports Tech. 2009;2(3–4):129–36.Google Scholar
  19. 19.
    Connaghan D, Kelly P, O’Connor N, et al. Multi-sensor classification of tennis strokes. In: IEEE conference on body sensor networks (BSN). Limerick; 2011. p. 1437–1440.Google Scholar
  20. 20.
    Ganter N, Krüger A, Gohla M, et al. Applicability of a full body inertial measurement system for kinematic analysis of the discus throw. ISBS Conf Proc Arch. 2010;1(1).Google Scholar
  21. 21.
    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. 2009;1(2):173–84.Google Scholar
  22. 22.
    Helten T, Brock H, Müller M, et al. Classification of trampoline jumps using inertial sensors. Sports Eng. 2011;14(2–4):155–64.CrossRefGoogle Scholar
  23. 23.
    Lai D, Hetchl M, Wei X, et al. On the difference in swing arm kinematics between low handicap golfers and non-golfers using wireless inertial sensors. Proc Eng. 2011;13:219–25.CrossRefGoogle Scholar
  24. 24.
    Lee J, Mellifont R, Burkett B, et al. Detection of illegal race walking: a tool to assist coaching and judging. Sensors. 2013;13(12):16065–74.PubMedCentralPubMedCrossRefGoogle Scholar
  25. 25.
    Ghasemzadeh H, Jafari R. Coordination analysis of human movements with body sensor networks: a signal processing model to evaluate baseball swings. IEEE Sens J. 2011;11(3):603–10.CrossRefGoogle Scholar
  26. 26.
    Gabbett T, Jenkins D, Abernethy B. Physical collisions and injury during professional rugby league skills training. J Sci Med Sport. 2010;13(6):578–83.PubMedCrossRefGoogle Scholar
  27. 27.
    Gastin P, Breed R, McLean O, et al. Quantification of tackling demands in elite Australian football using integrated wearable athlete technology. J Sci Med Sport. 2013;16(6):281.CrossRefGoogle Scholar
  28. 28.
    Gastin P, Mclean O, Breed R, et al. Tackle and impact detection in elite Australian football using wearable microsensor technology. J Sports Sci. 2014;32(10):947–53.PubMedCrossRefGoogle Scholar
  29. 29.
    Koda H, Sagawa K, Kuroshima K, et al. 3D measurement of forearm and upper arm during throwing motion using body mounted sensor. J Adv Mech Des Syst. 2010;4(1):167–78.Google Scholar
  30. 30.
    Kelly D, Coughlan G, Green B, et al. Automatic detection of collisions in elite level rugby union using a wearable sensing device. Sports Eng. 2012;15(2):81–92.CrossRefGoogle Scholar
  31. 31.
    McNamara D, Gabbett T, Chapman P, et al. The validity of microsensors to automatically detect bowling events and counts in cricket fast bowlers. Int J Sports Physiol Perform. 2015;10:71–5.PubMedCrossRefGoogle Scholar
  32. 32.
    Beanland E, Main L, Aisbett B, et al. Validation of GPS and accelerometer technology in swimming. J Sci Med Sport. 2014;17(2):234–8.PubMedCrossRefGoogle Scholar
  33. 33.
    Dadashi F, Crettenand F, Millet G, et al. Front-crawl instantaneous velocity estimation using a wearable inertial measurement unit. Sensors. 2012;12(10):12927–39.PubMedCentralPubMedCrossRefGoogle Scholar
  34. 34.
    Dadashi F, Crettenand F, Millet G, et al. Automatic front-crawl temporal phase detection using adaptive filtering of inertial signals. J Sports Sci. 2013;31(11):1251–60.PubMedCrossRefGoogle Scholar
  35. 35.
    Fulton S, Pyne D, Burkett B. Validity and reliability of kick count and rate in freestyle using inertial sensor technology. J Sports Sci. 2009;27(10):1051–8.PubMedCrossRefGoogle Scholar
  36. 36.
    Fulton S, Pyne D, Burkett B. Quantifying freestyle kick-count and kick-rate patterns in Paralympic swimming. J Sports Sci. 2009;27(13):1455–61.PubMedCrossRefGoogle Scholar
  37. 37.
    James D, Leadbetter R, Neeli M, et al. An integrated swimming monitoring system for the biomechanical analysis of swimming strokes. Sports Tech. 2011;4(3–4):141–50.Google Scholar
  38. 38.
    Jensen U, Prade F, Eskofier B. Classification of kinematic swimming data with emphasis on resource consumption. In: IEEE conference on body sensor networks (BSN). Cambridge, MA; 2013. p. 1–5.Google Scholar
  39. 39.
    Stamm A, James D, Burkett B, et al. Determining maximum push-off velocity in swimming using accelerometers. Proc Eng. 2013;60:201–7.CrossRefGoogle Scholar
  40. 40.
    Chardonnens J, Favre J, Gremion G, et al. A new method for unconstrained measurement of knee joint angle and timing in alpine skiing: comparison of crossover and crossunder turns. ISBS Conf Proc Arch. 2010;28:1–5.Google Scholar
  41. 41.
    Chardonnens J, Favre J, Le Callennec B, et al. Automatic measurement of key ski jumping phases and temporal events with a wearable system. J Sport Sci. 2012;30(1):53–61.CrossRefGoogle Scholar
  42. 42.
    Chardonnens J, Favre J, Cuendet F, et al. Characterization of lower-limbs inter-segment coordination during the take-off extension in ski jumping. Hum Mov Sci. 2013;32(4):741–52.PubMedCrossRefGoogle Scholar
  43. 43.
    Harding J, Mackintosh C, Hahn A, et al. Classification of aerial acrobatics in elite half-pipe snowboarding using body mounted inertial sensors. Eng Sport. 2008;7:447–56.Google Scholar
  44. 44.
    Marsland F, Lyons K, Anson J, et al. Identification of cross-country skiing movement patterns using micro-sensors. Sensors. 2012;12(4):5047–66.PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ryan Chambers
    • 1
    • 2
  • Tim J. Gabbett
    • 2
    • 3
  • Michael H. Cole
    • 2
  • Adam Beard
    • 4
  1. 1.Welsh Rugby UnionCardiffWales
  2. 2.School of Exercise ScienceAustralian Catholic UniversityBrisbaneAustralia
  3. 3.School of Human Movement StudiesThe University of QueenslandBrisbaneAustralia
  4. 4.University of LausanneLausanneSwitzerland

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