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Yüzmede Kol Çekişi ve Ayak Vuruşu Sürelerini Belirlemede Giyilebilir Atalet Ölçüm Birimlerini Kullanarak Basitleştirilmiş Bir Yöntem

Year 2023, , 142 - 149, 30.04.2023
https://doi.org/10.53434/gbesbd.1195632

Abstract

Atalet ölçüm birimlerinin (IMU) antrenörler tarafından kullanımı özellikle veri işleme sürecinin karmaşıklığı nedeniyle istenilen düzeye ulaşamamıştır. Çalışmanın amacı IMU’lardan elde edilen ham ivmelenme verilerinin basit filtreleme yöntemleri ile işlenmesinin ardından alanda faaliyet gösteren bireyler tarafından yüzmede teknik analizlerde kullanılabileceğinin ortaya koyulmasıdır. Bu amaç doğrultusunda IMU’lardan elde edilen ivmelenme verilerinden yüzücülerin kol çekişi ve ayak vuruşu süreleri belirlenmiş ve video kayıtlarından elde edilen süreler ile uyumu incelenmiştir. Çalışmada 5 kadın (18.2±.84 yıl; 1.69±.04 m; 60.76±1.86 kg) ve 5 erkek (19.6±2.41 yıl; 1.81±.03 m; 81.2±2.69 kg) müsabık yüzücü katılmcı olarak yer almıştır. İvemelenme ve görüntü verileri, yüzücülerin ayak ve el bileklerine bilateral olarak yerleştirilen dört adet IMU ve iki adet yüksek hızlı kamera ile toplanmıştır. Uyumun incelenmesinde Bland-Altman yöntemi kullanılmıştır. İki ölçüm arasındaki farkın “0” dan önemli ölçüde farklı olup olmadığını test etmek için ise Tek Örneklem T-testi kullanılmıştır. Kol çekişi ve ayak vuruşu döngüsü sürelerindeki farklılıkların çoğu (4’ü hariç) uyum sınırları içinde yer almıştır. T-testleri iki farklı ölçüm yönteminden elde edilen veriler arasındaki tüm farklılıkların 0'dan farklı olmadığını göstermiştir (p>.05). Sonuçlar, ivmeölçer verilerinin tek başına, diğer verilerle birleştirilmeden veya karmaşık algoritmalarla işlenmeden yüzme tekniklerinin zamansal değişkenlerini incelemek için kolaylıkla kullanılabileceğini göstermiştir.

Supporting Institution

Nevşehir Hacı Bektaş Veli Üniversitesi

Project Number

NEUBAP16S20

References

  • 1. Ahmad, N., Ghazilla, R. A. R., Khairi, N. M., & Kasi, V. (2013). Reviews on various inertial measurement unit (IMU) sensor applications. International Journal of Signal Processing Systems, 1(2), 256-262.
  • 2. Bächlin, M., & Tröster, G. (2012). Swimming performance and technique evaluation with wearable acceleration sensors. Pervasive and Mobile Computing, 8(1), 68-81.
  • 3. Beanland, E., Main, L. C., Aisbett, B., Gastin, P., & Netto, K. (2014). Validation of GPS and accelerometer technology in swimming. Journal of Science and Medicine in Sport, 17(2), 234-238.
  • 4. Callaway, A. J. (2015). Measuring kinematic variables in front crawl swimming using accelerometers: A validation study. Sensors, 15(5), 11363-11386.
  • 5. Callaway, A. J., Cobb, J. E., & Jones, I. (2009). A comparison of video and accelerometer based approaches applied to performance monitoring in swimming. International Journal of Sports Science & Coaching, 4(1), 139-153.
  • 6. Ceseracciu, E., Sawacha, Z., Fantozzi, S., Cortesi, M., Gatta, G., Corazza, S., & Cobelli, C. (2011). Markerless analysis of front crawl swimming. Journal of Biomechanics, 44(12), 2236-2242.
  • 7. Davey, N., Anderson, M., & James, D. A. (2008). Validation trial of an accelerometer‐based sensor platform for swimming. Sports Technology, 1(4-5), 202-207.
  • 8. Engel, A., Ploigt, R., Mattes, K., & Schaffert, N. (2021). Intra-cyclic analysis of the butterfly swimming technique using an inertial measurement unit. Journal of Sport and Human Performance, 9(2), 1-19.
  • 9. Engel, A., Schaffert, N., Ploigt, R., & Mattes, K. (2022). Intra-cyclic analysis of the front crawl swimming technique with an inertial measurement unit. Journal of Human Sport and Exercise, 17(3), 667-682.
  • 10. Guignard, B., Rouard, A., Chollet, D., & Seifert, L. (2017). Behavioral dynamics in swimming: The appropriate use of inertial measurement units. Frontiers in Psychology, 8, 383.
  • 11. Hamidi Rad, M., Gremeaux, V., Dadashi, F., & Aminian, K. (2021). A Novel macro-micro approach for swimming analysis in main swimming techniques using IMU sensors. Frontiers in Bioengineering and Biotechnology, 8, 597738.
  • 12. Le Sage, T., Bindel, A., Conway, P., Justham, L., Slawson, S., & West, A. (2010). Development of a real time system for monitoring of swimming performance. Procedia Engineering, 2(2), 2707-2712.
  • 13. Lennox, J. W., Rayfield, J., & Steffen, B. (2006). Soccer skills and drills. USA: Human Kinetics.
  • 14. Magalhaes, F. A. d., Vannozzi, G., Gatta, G., & Fantozzi, S. (2015). Wearable inertial sensors in swimming motion analysis: A systematic review. Journal of Sports Sciences, 33(7), 732-745.
  • 15. Maglischo, E. W. (2003). Swimming fastest. USA: Human Kinetics.
  • 16. Marinho, D. A., Barbosa, T. M., & Neiva, H. P. (2013). Swimming, running, cycling and triathlon. In Routledge Handbook of Sports Performance Analysis (pp. 454-481), ebook: Routledge.
  • 17. Monnet, T., Samson, M., Bernard, A., David, L., & Lacouture, P. (2014). Measurement of three-dimensional hand kinematics during swimming with a motion capture system: a feasibility study. Sports Engineering, 17(3), 171-181.
  • 18. Mooney, R., Corley, G., Godfrey, A., Osborough, C., Newell, J., Quinlan, L. R., & ÓLaighin, G. (2016). Analysis of swimming performance: perceptions and practices of US-based swimming coaches. Journal of Sports Sciences, 34(11), 997-1005.
  • 19. Mooney, R., Corley, G., Godfrey, A., Osborough, C., Quinlan, L., & ÓLaighin, G. (2015). Application of video-based methods for competitive swimming analysis: a systematic review. Sports and Exercise Medicine, 1(5), 133-150.
  • 20. Nugent, F. J., Comyns, T. M., & Warrington, G. D. (2017). Quality versus quantity debate in swimming: perceptions and training practices of expert swimming coaches. Journal of Human Kinetics, 57(1), 147-158.
  • 21. O’Reilly, M., Caulfield, B., Ward, T., Johnston, W., & Doherty, C. (2018). Wearable inertial sensor systems for lower limb exercise detection and evaluation: a systematic review. Sports Medicine, 48(5), 1221-1246.
  • 22. Pansiot, J., Lo, B., & Yang, G.-Z. (2010, June). Swimming stroke kinematic analysis with BSN. Paper presented at the 2010 International Conference on Body Sensor Networks, Biopolis, Singapore.
  • 23. Pérez, P., Llana, S., Brizuela, G., & Encarnación, A. (2009). Effects of three feedback conditions on aerobic swim speeds. Journal of Sports Science & Medicine, 8(1), 30.
  • 24. Schaffert, N., Engel, A., Schlüter, S., & Mattes, K. (2019). The sound of the underwater dolphin-kick: developing real-time audio feedback in swimming. Displays, 59, 53-62.
  • 25. Silvatti, A. P., Cerveri, P., Telles, T., Dias, F. A., Baroni, G., & Barros, R. M. (2013). Quantitative underwater 3D motion analysis using submerged video cameras: accuracy analysis and trajectory reconstruction. Computer Methods in Biomechanics and Biomedical Engineering, 16(11), 1240-1248.
  • 26. Slawson, S., Justham, L., Conway, P., Le-Sage, T., & West, A. (2012). Characterizing the swimming tumble turn using acceleration data. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 226(1), 3-15.
  • 27. Stamm, A., James, D. A., & Thiel, D. V. (2013). Velocity profiling using inertial sensors for freestyle swimming. Sports Engineering, 16(1), 1-11.
  • 28. Stamm, A., & Shlyonsky, I. (2020, November). Freestyle swimming analysis of symmetry and velocities using a mems based imu: introducing a symmetry score. Paper presented at the icSPORTS, Budapest, Hungary.
  • 29. Tarasevičius, D., & Serackis, A. (2020, April). Deep learning model for sensor based swimming style recognition. Paper presented at the 2020 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), Vilnius, Lithuania.
  • 30. Tolza, X., Soto-Romero, G., Fourniols, J.-Y., & Acco, P. (2017). Preliminary study: IMU system validation for real-time feedback on swimming technique. Computer Methods in Biomechanics and Biomedical Engineering, 20(sup1), S203-S204.
  • 31. Wang, Z., Shi, X., Wang, J., Gao, F., Li, J., Guo, M., . . . Qiu, S. (2019, October). Swimming motion analysis and posture recognition based on wearable inertial sensors. Paper presented at the 2019 IEEE international conference on systems, man and cybernetics (SMC). Bari, Italy.
  • 32. Wilson, B. D. (2008). Development in video technology for coaching. Sports Technology, 1(1), 34-40.
  • 33. Worsey, M. T., Pahl, R., Espinosa, H. G., Shepherd, J. B., & Thiel, D. V. (2021). Is machine learning and automatic classification of swimming data what unlocks the power of inertial measurement units in swimming? Journal of ports sciences, 39(18), 2095-2114.
  • 34. Zhang, Z., Xu, D., Zhou, Z., Mai, J., He, Z., & Wang, Q. (2017, October). IMU-based underwater sensing system for swimming stroke classification and motion analysis. Paper presented at the 2017 IEEE International Conference on Cyborg and Bionic Systems (CBS). Benjing, China.

A Simplified Method for Determining Swimming Arm-Stroke and Kick Durations Using Wearable Inertial Measurement Units

Year 2023, , 142 - 149, 30.04.2023
https://doi.org/10.53434/gbesbd.1195632

Abstract

The use of inertial measurement units (IMU) by the coaches has not reached the desired level, especially due to the complexity of the data processing. The aim of the study is to demonstrate that raw acceleration data obtained from IMUs can be used in swimming technical analysis by individuals operating in the field after processing with simple filtering methods. For this aim, the arm-stroke and kicking durations of the swimmers were determined using the acceleration data obtained from the IMUs and the agreement with the times obtained from the video recordings was examined. Five female (18.2±.84 years; 1.69±.04 m; 60.76±1.86kg) and 5 male (19.6±2.41 years; 1.81±.03 m; 81.2±2.69 kg) competitive swimmers participated to the study. Data was collected via two high-speed cameras and four IMUs which were placed bilaterally to the ankles and wrists of the swimmers. Bland-Altman method were used to examine the agreement. One-Sample T-tests were used to test whether the difference between the two measurements differed significantly from the “0”. The majority (except 4) of the differences in arm-stroke and kicking cycle durations were within the limits of agreement. T-tests indicated that all the differences between the data obtained from two different measurement methods were not different from 0 (p>.05). Results showed that the accelerometer data alone, without fusion with other data or processed with complex algorithms can be used with ease for investigating temporal variables of swimming techniques.

Project Number

NEUBAP16S20

References

  • 1. Ahmad, N., Ghazilla, R. A. R., Khairi, N. M., & Kasi, V. (2013). Reviews on various inertial measurement unit (IMU) sensor applications. International Journal of Signal Processing Systems, 1(2), 256-262.
  • 2. Bächlin, M., & Tröster, G. (2012). Swimming performance and technique evaluation with wearable acceleration sensors. Pervasive and Mobile Computing, 8(1), 68-81.
  • 3. Beanland, E., Main, L. C., Aisbett, B., Gastin, P., & Netto, K. (2014). Validation of GPS and accelerometer technology in swimming. Journal of Science and Medicine in Sport, 17(2), 234-238.
  • 4. Callaway, A. J. (2015). Measuring kinematic variables in front crawl swimming using accelerometers: A validation study. Sensors, 15(5), 11363-11386.
  • 5. Callaway, A. J., Cobb, J. E., & Jones, I. (2009). A comparison of video and accelerometer based approaches applied to performance monitoring in swimming. International Journal of Sports Science & Coaching, 4(1), 139-153.
  • 6. Ceseracciu, E., Sawacha, Z., Fantozzi, S., Cortesi, M., Gatta, G., Corazza, S., & Cobelli, C. (2011). Markerless analysis of front crawl swimming. Journal of Biomechanics, 44(12), 2236-2242.
  • 7. Davey, N., Anderson, M., & James, D. A. (2008). Validation trial of an accelerometer‐based sensor platform for swimming. Sports Technology, 1(4-5), 202-207.
  • 8. Engel, A., Ploigt, R., Mattes, K., & Schaffert, N. (2021). Intra-cyclic analysis of the butterfly swimming technique using an inertial measurement unit. Journal of Sport and Human Performance, 9(2), 1-19.
  • 9. Engel, A., Schaffert, N., Ploigt, R., & Mattes, K. (2022). Intra-cyclic analysis of the front crawl swimming technique with an inertial measurement unit. Journal of Human Sport and Exercise, 17(3), 667-682.
  • 10. Guignard, B., Rouard, A., Chollet, D., & Seifert, L. (2017). Behavioral dynamics in swimming: The appropriate use of inertial measurement units. Frontiers in Psychology, 8, 383.
  • 11. Hamidi Rad, M., Gremeaux, V., Dadashi, F., & Aminian, K. (2021). A Novel macro-micro approach for swimming analysis in main swimming techniques using IMU sensors. Frontiers in Bioengineering and Biotechnology, 8, 597738.
  • 12. Le Sage, T., Bindel, A., Conway, P., Justham, L., Slawson, S., & West, A. (2010). Development of a real time system for monitoring of swimming performance. Procedia Engineering, 2(2), 2707-2712.
  • 13. Lennox, J. W., Rayfield, J., & Steffen, B. (2006). Soccer skills and drills. USA: Human Kinetics.
  • 14. Magalhaes, F. A. d., Vannozzi, G., Gatta, G., & Fantozzi, S. (2015). Wearable inertial sensors in swimming motion analysis: A systematic review. Journal of Sports Sciences, 33(7), 732-745.
  • 15. Maglischo, E. W. (2003). Swimming fastest. USA: Human Kinetics.
  • 16. Marinho, D. A., Barbosa, T. M., & Neiva, H. P. (2013). Swimming, running, cycling and triathlon. In Routledge Handbook of Sports Performance Analysis (pp. 454-481), ebook: Routledge.
  • 17. Monnet, T., Samson, M., Bernard, A., David, L., & Lacouture, P. (2014). Measurement of three-dimensional hand kinematics during swimming with a motion capture system: a feasibility study. Sports Engineering, 17(3), 171-181.
  • 18. Mooney, R., Corley, G., Godfrey, A., Osborough, C., Newell, J., Quinlan, L. R., & ÓLaighin, G. (2016). Analysis of swimming performance: perceptions and practices of US-based swimming coaches. Journal of Sports Sciences, 34(11), 997-1005.
  • 19. Mooney, R., Corley, G., Godfrey, A., Osborough, C., Quinlan, L., & ÓLaighin, G. (2015). Application of video-based methods for competitive swimming analysis: a systematic review. Sports and Exercise Medicine, 1(5), 133-150.
  • 20. Nugent, F. J., Comyns, T. M., & Warrington, G. D. (2017). Quality versus quantity debate in swimming: perceptions and training practices of expert swimming coaches. Journal of Human Kinetics, 57(1), 147-158.
  • 21. O’Reilly, M., Caulfield, B., Ward, T., Johnston, W., & Doherty, C. (2018). Wearable inertial sensor systems for lower limb exercise detection and evaluation: a systematic review. Sports Medicine, 48(5), 1221-1246.
  • 22. Pansiot, J., Lo, B., & Yang, G.-Z. (2010, June). Swimming stroke kinematic analysis with BSN. Paper presented at the 2010 International Conference on Body Sensor Networks, Biopolis, Singapore.
  • 23. Pérez, P., Llana, S., Brizuela, G., & Encarnación, A. (2009). Effects of three feedback conditions on aerobic swim speeds. Journal of Sports Science & Medicine, 8(1), 30.
  • 24. Schaffert, N., Engel, A., Schlüter, S., & Mattes, K. (2019). The sound of the underwater dolphin-kick: developing real-time audio feedback in swimming. Displays, 59, 53-62.
  • 25. Silvatti, A. P., Cerveri, P., Telles, T., Dias, F. A., Baroni, G., & Barros, R. M. (2013). Quantitative underwater 3D motion analysis using submerged video cameras: accuracy analysis and trajectory reconstruction. Computer Methods in Biomechanics and Biomedical Engineering, 16(11), 1240-1248.
  • 26. Slawson, S., Justham, L., Conway, P., Le-Sage, T., & West, A. (2012). Characterizing the swimming tumble turn using acceleration data. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 226(1), 3-15.
  • 27. Stamm, A., James, D. A., & Thiel, D. V. (2013). Velocity profiling using inertial sensors for freestyle swimming. Sports Engineering, 16(1), 1-11.
  • 28. Stamm, A., & Shlyonsky, I. (2020, November). Freestyle swimming analysis of symmetry and velocities using a mems based imu: introducing a symmetry score. Paper presented at the icSPORTS, Budapest, Hungary.
  • 29. Tarasevičius, D., & Serackis, A. (2020, April). Deep learning model for sensor based swimming style recognition. Paper presented at the 2020 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), Vilnius, Lithuania.
  • 30. Tolza, X., Soto-Romero, G., Fourniols, J.-Y., & Acco, P. (2017). Preliminary study: IMU system validation for real-time feedback on swimming technique. Computer Methods in Biomechanics and Biomedical Engineering, 20(sup1), S203-S204.
  • 31. Wang, Z., Shi, X., Wang, J., Gao, F., Li, J., Guo, M., . . . Qiu, S. (2019, October). Swimming motion analysis and posture recognition based on wearable inertial sensors. Paper presented at the 2019 IEEE international conference on systems, man and cybernetics (SMC). Bari, Italy.
  • 32. Wilson, B. D. (2008). Development in video technology for coaching. Sports Technology, 1(1), 34-40.
  • 33. Worsey, M. T., Pahl, R., Espinosa, H. G., Shepherd, J. B., & Thiel, D. V. (2021). Is machine learning and automatic classification of swimming data what unlocks the power of inertial measurement units in swimming? Journal of ports sciences, 39(18), 2095-2114.
  • 34. Zhang, Z., Xu, D., Zhou, Z., Mai, J., He, Z., & Wang, Q. (2017, October). IMU-based underwater sensing system for swimming stroke classification and motion analysis. Paper presented at the 2017 IEEE International Conference on Cyborg and Bionic Systems (CBS). Benjing, China.
There are 34 citations in total.

Details

Primary Language English
Subjects Sports Medicine
Journal Section Articles
Authors

Uğur Ödek 0000-0003-0335-7956

Kürşat Özcan 0000-0002-4463-1272

Project Number NEUBAP16S20
Publication Date April 30, 2023
Submission Date October 27, 2022
Acceptance Date March 18, 2023
Published in Issue Year 2023

Cite

APA Ödek, U., & Özcan, K. (2023). A Simplified Method for Determining Swimming Arm-Stroke and Kick Durations Using Wearable Inertial Measurement Units. Gazi Beden Eğitimi Ve Spor Bilimleri Dergisi, 28(2), 142-149. https://doi.org/10.53434/gbesbd.1195632

Gazi Beden Eğitimi ve Spor Bilimleri Dergisi yılda dört kez yayımlanan bilimsel ve hakemli bir dergidir.