Sports Engineering

, Volume 16, Issue 1, pp 1–11 | Cite as

Velocity profiling using inertial sensors for freestyle swimming

Original Article

Abstract

The ability to unobtrusively measure velocity in the aquatic environment is a fundamental challenge for engineers and sports scientists and important in assessing the skill level. The aim of this research was to develop a method for velocity profiling in freestyle swimming utilising a purpose-built inertial sensor. Seventeen swimmers with different experience levels participated in this study performing a total of 159 laps in the velocity range from 0.79 to 2.04 m s−1. Data were collected using a triaxial accelerometer and a tethered velocity meter. The collected acceleration data were filtered using a 0.5 Hz Hamming-windowed FIR filter to remove the gravitational acceleration before the lap velocity profiles were calculated. These calculated lap velocity profiles were then compared with the velocity profiles measured by the velocity meter using Bland–Altman analysis. The scattering follows a normal distribution with a mean skewness of 0.96 ± 0.47 and kurtosis of 2.93 ± 1.12. The results show that an inertial sensor alone can be used to determine a lap velocity profile from single point acceleration records.

Keywords

Swimming Inertial sensors Velocity profile Acceleration Velocity variation Freestyle Intra-stroke velocity 

Abbreviation

SR

stroke rate (cycles/min)

References

  1. 1.
    Craig ABJ, Termin B, Pendergast DR (2006) Simultaneous recordings of velocity and video during swimming. Port J Sport Sci 6:32–36Google Scholar
  2. 2.
    Psycharakis SG, Naemi R, Connaboy C, McCabe C, Sanders RH (2009) Three-dimensional analysis of intracycle velocity fluctuations in frontcrawl swimming. Scand J Med Sci Sport 20(1):128–135. doi:10.1111/j.1600-0838.2009.00891 CrossRefGoogle Scholar
  3. 3.
    Le Sage T, Conway P, Justham L, Slawson S, Bindel A, West A (2010) A component based integrated system for signal processing of swimming performance. Paper presented at the SIGMAP, Athen, 28 June 2010Google Scholar
  4. 4.
    Rejman M, Borowska G (2007) Searching for criteria in evaluating the monofin swimming turn from the perspective of coaching and improving technique. J Sports Sci Med 7(1):11Google Scholar
  5. 5.
    Toshiaki G, Kaeko S, Hideki T, Teruo N, Atsunnori M, Osamu T, Shigehiro T, Yumiko O (2003) Forces and image analysis of gliding motion for beginners and competitive swimmers. In: Chatard JC (ed) Biomechanics and medicine in swimming IX, 2003 l’Universite de Saint-Etienne, Saint-Etienne, pp 127–131Google Scholar
  6. 6.
    Craig ABJ, Pendergast DR (1979) Relationships of stroke rate, distance per stroke, and velocity in competitive swimming. Med Sci Sports Exerc 11(3):278–283Google Scholar
  7. 7.
    James D, Davey N (2007) Swimming stroke analysis using multiple accelerometer devices and tethered systems. In: Fuss FK, Subic A, Ujihashi S (eds) The impact of technology on sport II, 2007 pp 577–582 doi:10.1201/9781439828427.ch83
  8. 8.
    Swift Performance Equipment (2006) Swift sports speed probe 5000 V. http://www.spe.com.au/. Accessed 24 Aug 2010
  9. 9.
    Stamm A, Thiel D, Burkett BJ, James DA (2009) Roadmapping performance enhancement measures and technology in swimming. Impact Technol Sport II:213–217Google Scholar
  10. 10.
    Stamm A, Thiel DV, Burkett B, James DA (2011) Towards determining absolute velocity of freestyle swimming using 3-axis accelerometers. Procedia Eng 13:120–125. doi:10.1016/j.proeng.2011.05.061 CrossRefGoogle Scholar
  11. 11.
    Davey N, Anderson M, James DA (2008) Validation trial of an accelerometer-based sensor platform for swimming. J Sports Technol 1(4–5):202–207. doi:10.1002/jst.59 CrossRefGoogle Scholar
  12. 12.
    Le Sage T, Bindel A, Conway P, Justham L, Slawson S, West A (2011) Embedded programming and real-time signal processing of swimming strokes. Sports Eng 14(1):1–14. doi:10.1007/s12283-011-0070-7 CrossRefGoogle Scholar
  13. 13.
    Ohgi Y (2002) Microcomputer-based acceleration sensor device for sports biomechanics—stroke evaluation by using swimmer’s wrist acceleration. In: Proceedings of IEEE Sensors 2002, 2002. pp 699–704. doi:10.1109/icsens.2002.1037188
  14. 14.
    Ohgi Y, Yasumura M, Ichikawa H, Miyaji C (2002) Analysis of stroke technique using acceleration sensor IC in freestyle swimming. Eng Sport 7 2:503–511Google Scholar
  15. 15.
    Ohgi Y, Ichikawa H, Homma M, Miyaji C (2003) Stroke phase discrimination in breaststroke swimming using a tri-axial acceleration sensor device. Sports Eng 6(2):113–123. doi:10.1007/bf02903532 CrossRefGoogle Scholar
  16. 16.
    Pansiot J, Lo B, Guang-Zhong Y (2010) Swimming stroke kinematic analysis with BSN. In: International Conference on Body Sensor Networks (BSN), 2010 pp 153–158Google Scholar
  17. 17.
    Lai A, James DA, Hayes JP, Harvey EC (2004) Semi-automatic calibration technique using six inertial frames of reference. In: Microelectronics: Design, Technology, and Packaging, Perth, 2004 vol 1. SPIE, pp 531–542. doi: 10.1117/12.530199
  18. 18.
    Callaway A, Cobb J, Jones I (2009) A comparison of video and accelerometer based approaches applied to performance monitoring in swimming. Int J Sports Sci Coach 4(1):139–153CrossRefGoogle Scholar
  19. 19.
    Daukantas S, Marozas V, Lukosevicius A (2008) Inertial sensor for objective evaluation of swimmer performance. In: 11th International Biennial Baltic Electronics Conference 2008, pp 321–324Google Scholar
  20. 20.
    Bächlin M, Tröster G (2011) Swimming performance and technique evaluation with wearable acceleration sensors. Pervasive Mob Comput 8(1):68–81. doi:10.1016/j.pmcj.2011.05.003 CrossRefGoogle Scholar
  21. 21.
    STMicroelectronics (2009) http://www.st.com. Accessed 02 Aug 2009
  22. 22.
    NordicSemiconducter (2008) http://www.nordicsemi.com/. Accessed 02 Aug 2009
  23. 23.
    Atmel (2009) http://www.atmel.com/. Accessed 02 Aug 2009
  24. 24.
    James DA, Leadbetter RI, Neeli MR, Burkett BJ, Thiel DV, Lee JB (2011) An integrated swimming monitoring system for the biomechanical analysis of swimming strokes. Sports Technol 4(3–4):141–150. doi: 10.1080/19346182.2012.725410 Google Scholar
  25. 25.
    James DA, Wixted A (2011) ADAT: a Matlab toolbox for handling time series athlete performance data. Procedia Eng 13:451–456. doi:10.1016/j.proeng.2011.05.113 CrossRefGoogle Scholar
  26. 26.
    Davey N, James D, Wixted A, Ohgi Y (2008) A low cost self contained platform for human motion analysis. In: Fuss FK, Subic A, Ujihashi S (eds) The Impact of technology on sport II. Taylor & Francis, London, pp 101–111. doi:10.1201/9781439828427.ch14 Google Scholar
  27. 27.
    Altman DG, Bland JM (1983) Measurement in medicine: the analysis of method comparison studies. J R Stat Soc 32(3):307–317Google Scholar
  28. 28.
    Maglischo EW (2003) Swimming Fastest, 3rd edn. Human Kinetics, ChampaignGoogle Scholar

Copyright information

© International Sports Engineering Association 2012

Authors and Affiliations

  1. 1.Centre for Wireless Monitoring and ApplicationsGriffith UniversityBrisbaneAustralia

Personalised recommendations