Validity of a Microsensor-Based Algorithm for Detecting Scrum Events in Rugby Union

in International Journal of Sports Physiology and Performance
Restricted access

Purchase article

USD  $24.95

Student 1 year subscription

USD  $107.00

1 year subscription

USD  $142.00

Student 2 year subscription

USD  $203.00

2 year subscription

USD  $265.00

Purpose: Commercially available microtechnology devices containing accelerometers, gyroscopes, magnetometers, and global positioning technology have been widely used to quantify the demands of rugby union. This study investigated whether data derived from wearable microsensors can be used to develop an algorithm that automatically detects scrum events in rugby union training and match play. Methods: Data were collected from 30 elite rugby players wearing a Catapult OptimEye S5 (Catapult Sports, Melbourne, Australia) microtechnology device during a series of competitive matches (n = 46) and training sessions (n = 51). A total of 97 files were required to “train” an algorithm to automatically detect scrum events using random forest machine learning. A further 310 files from training (n = 167) and match-play (n = 143) sessions were used to validate the algorithm’s performance. Results: Across all positions (front row, second row, and back row), the algorithm demonstrated good sensitivity (91%) and specificity (91%) for training and match-play events when the confidence level of the random forest was set to 50%. Generally, the algorithm had better accuracy for match-play events (93.6%) than for training events (87.6%). Conclusions: The scrum algorithm was able to accurately detect scrum events for front-row, second-row, and back-row positions. However, for optimal results, practitioners are advised to use the recommended confidence level for each position to limit false positives. Scrum algorithm detection was better with scrums involving ≥5 players and is therefore unlikely to be suitable for scrums involving 3 players (eg, rugby sevens). Additional contact- and collision-detection algorithms are required to fully quantify rugby union demands.

Chambers is with Welsh Rugby Union, Cardiff, United Kingdom. Chambers and Cole are with the School of Exercise Science, Australian Catholic University, Brisbane, QLD, Australia. Gabbett is with Gabbett Performance Solutions, Brisbane, QLD, Australia, and the Inst for Resilient Regions, University of Southern Queensland, Ipswich, QLD, Australia.

Chambers (ryanchambers13@gmail.com) is corresponding author.
  • 1.

    Cunniffe B, Proctor W, Baker J, Davies B. An evaluation of the physiological demands of elite rugby union using global positioning system tracking software. J Strength Cond Res. 2009;23(4):11951203. PubMed ID: 19528840 doi:10.1519/JSC.0b013e3181a3928b

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Lacome M, Piscione J, Hager J, Bourdin M. A new approach to quantifying physical demand in rugby union. J Sports Sci. 2013;32(3):290300. PubMed ID: 24016296 doi:10.1080/02640414.2013.823225

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Austin D, Gabbett T, Jenkins D. The physical demands of Super 14 rugby union. J Sci Med Sport. 2011;14(3):259263. PubMed ID: 21324741 doi:10.1016/j.jsams.2011.01.003

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Coughlan G, Green B, Pook P, Toolan E, O’Connor S. Physical game demands in elite rugby union: a global positioning system analysis and possible implications for rehabilitation. J Orthop Sports Phys Ther. 2011;41(8):600605. PubMed ID: 21654094 doi:10.2519/jospt.2011.3508

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5.

    Quarrie K, Hopkins W, Anthony M, Gill N. Positional demands of international rugby union: evaluation of player actions and movements. J Sci Med Sport. 2013;16(4):353359. PubMed ID: 22975233 doi:10.1016/j.jsams.2012.08.005

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Duthie G, Pyne D, Hooper S. Applied physiology and game analysis of rugby union. Sports Med. 2003;33(13):973991. PubMed ID: 14606925 doi:10.2165/00007256-200333130-00003

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Cahill N, Lamb K, Worsfold P, Headey R, Murray S. The movement characteristics of English Premiership rugby union players. J Sports Sci. 2013;31(3):229237. PubMed ID: 23009129 doi:10.1080/02640414.2012.727456

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Roberts SP, Trewartha G, Higgitt RJ, El-Abd J, Stokes KA. The physical demands of elite English rugby union. J Sports Sci. 2008;26(8):825833. PubMed ID: 18569548 doi:10.1080/02640410801942122

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9.

    Wu W, Chang J, Wu J, Guo L. An investigation of rugby scrimmaging posture and individual maximum pushing force. J Strength Cond Res. 2007;21(1):251258. PubMed ID: 17313278 doi:10.1519/00124278-200702000-00045

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    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):23192322. PubMed ID: 23090320 doi:10.1519/JSC.0b013e318277fd21

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Chambers R, Gabbett T, Cole M, Beard A. The use of wearable microsensors to quantify sport-specific movements. Sports Med. 2015;45(7):10651081. PubMed ID: 25834998 doi:10.1007/s40279-015-0332-9

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    McNamara D, Gabbett T, Chapman P, Naughton G, Farhart P. The validity of microsensors to automatically detect bowling events and counts in cricket fast bowlers. Int J Sports Physiol Perform. 2015;10(1):7175. PubMed ID: 24911322 doi:10.1123/ijspp.2014-0062

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Murray NB, Black GM, Whiteley RJ, et al. Automatic detection of pitching and throwing events in baseball with inertial measurement sensors. Int J Sports Physiol Perform. 2017;12(4):533537. PubMed ID: 27617847 doi:10.1123/ijspp.2016-0212

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14.

    Gabbett T, Jenkins D, Abernethy B. Physical collisions and injury during professional rugby league skills training. J Sci Med Sport. 2010;13(6):578583. PubMed ID: 20483661 doi:10.1016/j.jsams.2010.03.007

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Hulin B, Gabbett T, Johnston R, Jenkins D. Wearable microtechnology can accurately identify collision events during professional rugby league match-play. J Sci Med Sport. 2017;20(7):638642. PubMed ID: 28153609 doi:10.1016/j.jsams.2016.11.006

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Kelly D, Coughlan GF, Green BS, Caulfield B. Automatic detection of collisions in elite level rugby union using a wearable sensing device. Sports Eng. 2012;15(2):8192. doi:10.1007/s12283-012-0088-5

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 17.

    Taylor AE, Kemp S, Trewartha G, Stokes KA. Scrum injury risk in English professional rugby union. Br J Sports Med. 2014;48(13):10661068. PubMed ID: 24603079 doi:10.1136/bjsports-2013-092873

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Moore IS, Ranson C, Mathema P. Injury risk in international rugby union: three-year injury surveillance of the Welsh National Team. Orthop J Sports Med. 2015;3(7):2325967115596194. doi:10.1177/2325967115596194

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Hartwig T, Naughton G, Searl J. Motion analyses of adolescent rugby union players: a comparison of training and game demands. J Strength Cond Res. 2011;25(4):966972. PubMed ID: 20647941 doi:10.1519/JSC.0b013e3181d09e24

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Quarrie KL, Raftery M, Blackie J, et al. Managing player load in professional rugby union: a review of current knowledge and practices. Br J Sports Med. 2016;51(5):421427. PubMed ID: 27506436 doi:10.1136/bjsports-2016-096191

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21.

    Reardon C, Tobin D, Tierney P, Delahunt E. Collision count in rugby union: a comparison of micro-technology and video analysis methods. J Sports Sci. 2017;35(20):20282034. PubMed ID: 27868475 doi:10.1080/02640414.2016.1252051

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 22.

    Wundersitz D, Josman C, Gupta R, Netto K, Gastin P, Robertson S. Classification of team sport activities using a single wearable tracking device. J Biomech. 2015;48(15):39753981. PubMed ID: 26472301 doi:10.1016/j.jbiomech.2015.09.015

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Olivares A, Górriz J, Ramírez J, Olivares G. Using frequency analysis to improve the precision of human body posture algorithms based on Kalman filters. Comput Biol Med. 2016;72:229238. PubMed ID: 26337122 doi:10.1016/j.compbiomed.2015.08.007

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Bergamini E, Ligorio G, Summa A, Vannozzi G, Cappozzo A, Sabatini A. Estimating orientation using magnetic and inertial sensors and different sensor fusion approaches: accuracy assessment in manual and locomotion tasks. Sensors. 2014;14(10):1862518649. PubMed ID: 25302810 doi:10.3390/s141018625

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Cole MH, van den Hoorn W, Kavanagh JK, et al. Concurrent Validity of accelerations measured using a tri-axial inertial measurement unit while walking on firm, compliant and uneven surfaces. PLoS ONE. 2014;9(5):98395. PubMed ID: 24866262 doi:10.1371/journal.pone.0098395

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 26.

    Genuer R, Poggi J, Tuleau-Malot C. VSURF: an R package for variable selection using random forests. R J. 2015;7(2):1933.

  • 27.

    Breiman L. Random forests. Mach Learning. 2001;45(1):532. doi:10.1023/A:1010933404324

  • 28.

    McNamara D, Gabbett T, Blanch P, Kelly L. The relationship between variables in wearable microtechnology devices and cricket fast bowling intensity. Int J Sports Physiol Perform. 2018;13(2):135139. PubMed ID: 28488918 doi:10.1123/ijspp.2016-0540

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29.

    Whiteside D, Cant O, Connolly M, Reid M. Monitoring hitting load in tennis using inertial sensors and machine learning. Int J Sports Physiol Perform. 2017;12(9):12121217. PubMed ID: 28182523 doi:10.1123/ijspp.2016-0683

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30.

    Helten T, Brock H, Müller M, Seidel H. Classification of trampoline jumps using inertial sensors. Sports Eng. 2011;14(2–4):155164. doi:10.1007/s12283-011-0081-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 31.

    Lai D, Hetchl M, Wei X, Ball K, Mclaughlin P. On the difference in swing arm kinematics between low handicap golfers and non-golfers using wireless inertial sensors. Procedia Eng. 2011;13:219225. doi:10.1016/j.proeng.2011.05.076

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 32.

    Lee JB, Mellifont RB, Burkett BJ, James DA. Detection of illegal race walking: a tool to assist coaching and judging. Sensors. 2013;13(12):1606516074. PubMed ID: 24287531 doi:10.3390/s131216065

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Beanland E, Main L, Aisbett B, Gastin P, Netto K. Validation of GPS and accelerometer technology in swimming. J Sci Med Sport. 2014;17(2):234238. PubMed ID: 23707140 doi:10.1016/j.jsams.2013.04.007

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Dadashi F, Crettenand F, Millet GP, Aminian K. Front-crawl instantaneous velocity estimation using a wearable inertial measurement unit. Sensors. 2012;12(12):1292712939. PubMed ID: 23201978 doi:10.3390/s121012927

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35.

    Dadashi F, Crettenand F, Millet G, Seifert L, Komar J, Aminian K. Automatic front-crawl temporal phase detection using adaptive filtering of inertial signals. J Sports Sci. 2013;31(11):12511260. PubMed ID: 23560703 doi:10.1080/02640414.2013.778420

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Fulton SK, Pyne DB, Burkett B. Validity and reliability of kick count and rate in freestyle using inertial sensor technology. J Sports Sci. 2009;27(10):10511058. PubMed ID: 19642049 doi:10.1080/02640410902998247

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Fulton SK, Pyne DB, Burkett B. Quantifying freestyle kick-count and kick-rate patterns in Paralympic swimming. J Sports Sci. 2009;27(13):14551461. PubMed ID: 19787541 doi:10.1080/02640410903062936

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    James DA, Leadbetter RI, Neeli MR, Burkett BJ, Thiel DV, Lee JB. An integrated swimming monitoring system for the biomechanical analysis of swimming strokes. Sports Tech. 2011;4(3–4):141150. doi:10.1080/19346182.2012.725410

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 39.

    Chardonnens J, Favre J, Le Callennec B, Cuendet F, Gremion G, Aminian K. Automatic measurement of key ski jumping phases and temporal events with a wearable system. J Sports Sci. 2012;30(1):5361. PubMed ID: 22168430 doi:10.1080/02640414.2011.624538

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    Chardonnens J, Favre J, Cuendet F, Gremion G, Aminian K. Characterization of lower-limbs inter-segment coordination during the take-off extension in ski jumping. Hum Mov Sci. 2013;32(4):741752. PubMed ID: 23810716 doi:10.1016/j.humov.2013.01.010

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41.

    Marsland F, Lyons K, Anson J, Waddington G, Macintosh C, Chapman D. Identification of cross-country skiing movement patterns using micro-sensors. Sensors. 2012;12(12):50475066. PubMed ID: 22666075 doi:10.3390/s120405047

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Gastin P, Mclean O, Breed R, Spittle M. Tackle and impact detection in elite Australian football using wearable microsensor technology. J Sports Sci. 2014;32(10):947953. PubMed ID: 24499311 doi:10.1080/02640414.2013.868920

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 264 245 15
Full Text Views 24 21 3
PDF Downloads 14 14 7