Adoption of bio-image technology on rehabilitation intervention of sports injury of golf (original) (raw)

Abstract

This work aimed to explore the characteristics of surface electromyography (EMG) signal of golfers’ low back pain and the effect of rehabilitation. Based on the time-varying parameter autoregressive model and artificial neural network, ARAN algorithm was constructed, which was compared with the autoregressive moving average (ARMA) algorithm and the convolutional neural network (CNN) algorithm. Then, the established ARAN algorithm was employed to evaluate the characteristics of surface EMG signal of 106 golfers with low back pain. It was found that the accuracy, sensitivity, and specificity of the ARAN algorithm were superior to those of the CNN and ARMA algorithms. The golfer’s Roland-Morris Disability Questionnaire (RMDQ) score after treatment was less than that before treatment (P < 0.05). Moreover, there was significant negative correlation between RMDQ score and the mean values of time-varying parameters _a_1 and a_3 (P < 0.05). The RMDQ score had a very obvious positive correlation with the mean values of a_2, _a_4, and _a_6 (P < 0.001) and had a negative correlation with the mean value of _a_5 (P < 0.05). To sum up, the time-varying parameters of the surface EMG signal can effectively evaluate the golfer’s low back pain and the effect of treatment and rehabilitation.

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References

  1. Choi A, Sim T, Mun JH (2016) Improved determination of dynamic balance using the centre of mass and centre of pressure inclination variables in a complete golf swing cycle. J Sports Sci 34(10):906–914
    Article Google Scholar
  2. Kim JH, Han JK, Han DH (2018) Training effects of interactive metronome® on golf performance and brain activity in professional woman golf players. Hum Mov Sci 61:63–71
    Article Google Scholar
  3. Sim T, Choi A, Lee S et al (2017) How to quantify the transition phase during golf swing performance: torsional load affects low back complaints during the transition phase. J Sports Sci 35(20):2051–2059
    Article Google Scholar
  4. Zhang Z, Zhang Y, Kos A et al (2017) A sensor-based golfer-swing signature recognition method using linear support vector machine. Elektrotehniski Vestnik 84(5):247–252
    Google Scholar
  5. Cust EE, Sweeting AJ, Ball K et al (2019) Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance. J Sports Sci 37(5):568–600
    Article Google Scholar
  6. Sim T, Yoo H, Choi A et al (2017) Analysis of pelvis-thorax coordination patterns of professional and amateur golfers during golf swing. J Mot Behav 49(6):668–674
    Article Google Scholar
  7. Marshall B, Wright DJ (2016) Layered stimulus response training versus combined action observation and imagery: effects on golf putting performance and imagery ability characteristics. J Imag Res Sport Phys Act 11(1):35–46
    Google Scholar
  8. Needle AR, Lepley AS, Grooms DR (2017) Central nervous system adaptation after ligamentous injury: a summary of theories, evidence, and clinical interpretation. Sports Med 47(7):1271–1288
    Article Google Scholar
  9. Kim JH, Ridgel AL (2019) Effects of Interactive Metronome and golf swing mechanics training on technique and motor timing in professional and amateur golfers. Int J Sports Sci Coach 14(6):786–797
    Article Google Scholar
  10. Adesida Y, Papi E, McGregor AH (2019) Exploring the role of wearable technology in sport kinematics and kinetics: a systematic review. Sensors 19(7):1597
    Article Google Scholar
  11. Kubo Y, Watanabe K, Nakazato K et al (2019) The effect of a previous strain injury on regional neuromuscular activation within the rectus femoris. J Hum Kinet 27(66):89–97
    Article Google Scholar
  12. Contemori S, Biscarini A (2019) Isolated infraspinatus atrophy secondary to suprascapular nerve neuropathy results in altered shoulder muscles activity. J Sport Rehabilit 28(3):219–228
    Article Google Scholar
  13. Schuermans J, Danneels L, Van Tiggelen D et al (2017) Proximal neuromuscular control protects against hamstring injuries in male soccer players: a prospective study with electromyography time-series analysis during maximal sprinting. Am J Sports Med 45(6):1315–1325
    Article Google Scholar
  14. Tan SJ, Kerr G, Sullivan JP et al (2019) A brief review of the application of neuroergonomics in skilled cognition during expert sports performance. Front Hum Neurosci 13:278
    Article Google Scholar
  15. Camomilla V, Bergamini E, Fantozzi S et al (2018) Trends supporting the in-field use of wearable inertial sensors for sport performance evaluation: a systematic review. Sensors 18(3):873
    Article Google Scholar
  16. Purevsuren T, Khuyagbaatar B, Lee SK et al (2020) Biomechanical Factors Leading to High Loading in the Anterior Cruciate Ligament of the Lead Knee During Golf Swing. Int J Precis Eng Manuf 21(2):309–318
    Article Google Scholar
  17. Steffen K, Soligard T, Mountjoy M et al (2020) How do the new Olympic sports compare with the traditional Olympic sports? Injury and illness at the 2018 Youth Olympic summer games in Buenos Aires, Argentina. Br J of Sports Med 54(3):168–175
    Article Google Scholar
  18. Davies MAM, Lawrence T, Edwards A et al (2020) Serious sports-related injury in England and Wales from 2012–2017: a study protocol. Inj Epidemiol 7:1–10
    Article Google Scholar
  19. Cheon M, Khuyagbaatar B, Yeom JH et al (2020) Analysis of swing tempo, swing rhythm, and functional swing plane slope in golf with a wearable inertial measurement unit sensor. J Mech Sci Technol 34(7):3095–3101
    Article Google Scholar
  20. Kim HC, Park KJ (2020) Sports convergence analysis of sports injuries and heart rate variability in national female Judo athletes. J Korea Converg Soc 11(4):49–54
    Google Scholar
  21. Polmann H, Melo G, Conti Réus J et al (2020) Prevalence of dentofacial injuries among combat sports practitioners: a systematic review and meta-analysis. Dent Traumatol 36(2):124–140
    Article Google Scholar
  22. Choi H, Park S (2020) Three dimensional upper limb joint kinetics of a golf swing with measured internal grip force. Sensors 20(13):3672
    Article Google Scholar
  23. Font MM (2020) Clinical applications of nuclear medicine in the diagnosis and evaluation of musculoskeletal sports injuries. Revista Española de Medicina Nuclear e Imagen Molecular (English Edition) 39(2): 112-134.
  24. Khanpara S, Ruiz-Pardo D, Spence SC et al (2020) Incidence of cervical spine fractures on CT: a study in a large level I trauma center. Emerg Radiol 27(1):1–8
    Article Google Scholar
  25. Walker CT, Uribe JS, GPorter RW (2019) Golf: a contact sport. Repetitive traumatic discopathy may be the driver of early lumbar degeneration in modern-era golfers. J Neurosurg Spine 31(6):914–917
    Article Google Scholar
  26. Speariett S, Armstrong R (2019) The relationship between the golf-specific movement screen and golf performance. J Sport Rehabilit 29(4):425–435
    Article Google Scholar
  27. Carson HJ, Richards J, Mazuquin B (2019) Examining the influence of grip type on wrist and club head kinematics during the golf swing: benefits of a local co-ordinate system. Eur J Sport Sci 19(3):327–335
    Article Google Scholar
  28. Faux L, Carlisle A, Vickers J et al (2019) The effect of alterations in foot centre of pressure on lower body kinematics during the five-iron golf swing. J Sports Sci 37(17):2014–2020
    Article Google Scholar
  29. Rosselli AC (2019) Time for a reassessment of assumption of risk in golf? Don’t Count Phys Educ 76(4):1128–1133
    Google Scholar
  30. Kirkwood G, Hughes TC, Pollock AM (2019) Results on sports-related injuries in children from NHS emergency care dataset Oxfordshire pilot: an ecological study. J R Soc Med 112(3):109–118
    Article Google Scholar
  31. Sarmiento K, Thomas KE, Daugherty J et al (2019) Emergency department visits for sports-and recreation-related traumatic brain injuries among children-United States, 2010–2016. Morb Mortal Wkly Rep 68(10):237
    Article Google Scholar
  32. Chiementin X, Kouroussis G, Murer S et al (2019) Experimental modal analysis of hand-arm vibration in golf: influence of grip strength. Appl Sci 9(10):2050
    Article Google Scholar
  33. Baek YM (2019) The relationship among trust of instructors, exercise flow and psychological happiness for golf participants. J Converg Inf Technol 9(11):141–148
    Google Scholar
  34. Silva L, Vaz JR, Castro MA et al (2015) Recurrence quantification analysis and support vector machines for golf handicap and low back pain EMG classification. J Electromyogr Kinesiol 25(4):637–647
    Article Google Scholar
  35. Joyce C, Chivers P, Sato K et al (2016) Multi-segment trunk models used to investigate the crunch factor in golf and their relationship with selected swing and launch parameters. J Sports Sci 34(20):1970–1975
    Article Google Scholar

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Acknowledgements

This work was supported by Hainan Provincial Natural Science Foundation of China (No. 819QN262).

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Authors and Affiliations

  1. School of Physical Education, Qiongtai Normal University, Haikou, China
    Wenlong Zhou & Zhiyong Fu
  2. Key Laboratory of Child Cognition & Behavior Development of Hainan Province, Haikou, China
    Wenlong Zhou & Zhiyong Fu

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  1. Wenlong Zhou
  2. Zhiyong Fu

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Correspondence toZhiyong Fu.

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Zhou, W., Fu, Z. Adoption of bio-image technology on rehabilitation intervention of sports injury of golf.J Supercomput 77, 11310–11327 (2021). https://doi.org/10.1007/s11227-021-03732-5

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