Automated Assessment of Developmental Stage of Overhand Throwing Skill

Document Type : Original research papers

Authors

1 Department of Motor Behavior, Faculty of Physical Education and Sport Sciences, University of Tehran, Tehran, Iran

2 Department of motor behavior, Faculty of Physical Education and Sport Sciences, University of Tehran, Tehran, Iran

3 Department of motor behavior, Faculty of Physical Education and Sport Sciences, University of Tehran, Tehran, Iran.

4 Department of Interdisciplinary Technology /Network Science and Technology, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.

Abstract

The trade-off between speed and accuracy in scoring process-oriented tests for fundamental movement skills (FMS) has always been challenging for a screening project. The aim of this study was the feasibility of using wearable inertial measurement units (IMU) and artificial intelligence algorithms to automatically assessment of FMS. 123 overhand throwings were performed by children aged 4 to 10 years (age = 7±1.84) (53% = boys). Three IMU (Shokofa Tavan Vira) sent signals of angular velocity, linear acceleration of the preferred hand, non-predominant ankle, and lumbar region of the children. Each performance was scored according to the criteria of the third edition test of The Gross Motor Development (TGMD-3) by reviewing the video of the performed skills. The "k nearest neighbor" algorithm was used for automatic data classification. The minimum difference between test signals and training signals was calculated and classified. Two issues were assessed: false acceptance, in which an “incorrect” performance was classified as “correct”; and false rejection where a “correct” performance was classified as “incorrect”. The classification accuracy of the K-nearest neighbor (KNN) algorithm was 85%. The automatic scoring algorithm also correctly classified 93%, 78%, 93%, and 76% in criteria 1 to 4, respectively. Low-back IMU data analysis shows the model's accuracy of 75%. Further, the total scoring time was reduced from 5 minutes to less than 30 seconds. The use of artificial intelligence in the signal processing of only three IMU was a reliable and practical method for the assessment of FMS. This approach means the monitoring and evaluation of children's movement skills can be objective. In addition, while maintaining relative accuracy, the time involved in the process-oriented analysis of FMS for research, clinical, sports, and educational purposes was reduced entirely.

Keywords

Main Subjects

References

  1. Cattuzzo MT, dos Santos Henrique R, Ré AHN, de Oliveira IS, Melo BM, de Sousa Moura M, et al. Motor competence and health related physical fitness in youth: A systematic review. Journal of Science and Medicine in Sport. 2016;19:123-9.
  2. Engel AC, Broderick CR, van Doorn N, Hardy LL, Parmenter BJ. Exploring the Relationship Between Fundamental Motor Skill Interventions and Physical Activity Levels in Children: A Systematic Review and Meta-analysis. Sports Medicine. 2018;48:1845-57.
  3. Lopes L, Silva Mota JAP, Moreira C, Abreu S, Agostinis Sobrinho C, Oliveira-Santos J, et al. Longitudinal associations between motor competence and different physical activity intensities: LabMed physical activity study. Journal of Sports Sciences. 2019;37:285-90.
  4. Robinson LE, Stodden DF, Barnett LM, Lopes VP, Logan SW, Rodrigues LP, et al. Motor Competence and its Effect on Positive Developmental Trajectories of Health. Sports Medicine. 2015;45:1273-84.
  5. Logan SW, Ross SM, Chee K, Stodden DF, Robinson LE. Fundamental motor skills: A systematic review of terminology. Journal of Sports Sciences. 2018;36:781-96.
  6. Griffiths A, Toovey R, Morgan PE, Spittle AJ. Psychometric properties of gross motor assessment tools for children: A systematic review. BMJ Open. 2018; 8:e021734
  7. Utesch T, Bardid F, Büsch D, Strauss B. The Relationship Between Motor Competence and Physical Fitness from Early Childhood to Early Adulthood: A Meta-Analysis. Sports Medicine. 2019;49:541-51.
  8. Barnett LM, Minto C, Lander N, Hardy LL. Interrater reliability assessment using the Test of Gross Motor Development-2. Journal of Science and Medicine in Sport. 2014;17:667-70.
  9. Ward B, Thornton A, Lay B, Chen N, Rosenberg M. Can Proficiency Criteria Be Accurately Identified During Real-Time Fundamental Movement Skill Assessment? Research Quarterly for Exercise and Sport. 2020;91:64-72.
  10. Haynes J, Miller J. Preparing pre-service primary school teachers to assess fundamental motor skills: two skills and two approaches. Physical Education and Sport Pedagogy. 2015;20:397-408.
  11. Lander N, Morgan PJ, Salmon J, Barnett LM. Teachers’ perceptions of a fundamental movement skill (FMS) assessment battery in a school setting. Measurement in Physical Education and Exercise Science. 2016;20(1):50-62.
  12. Sorsdahl AB, Moe‐Nilssen R, Strand LI. Observer reliability of the Gross Motor Performance Measure and the Quality of Upper Extremity Skills Test, based on video recordings. Developmental Medicine & Child Neurology. 2008;50(2):146-51.
  13. Tian ZZ, Kyte MD, Messer CJ. Parallax error in video-image systems. Journal of Transportation Engineering. 2002;128(3):218-23.
  14. Bisi MC, Pacini Panebianco G, Polman R, Stagni R. Objective assessment of movement competence in children using wearable sensors: An instrumented version of the TGMD-2 locomotor subtest. Gait and Posture. 2017;56:42-8.
  15. Chambers R, Gabbett TJ, Cole MH, Beard A. The use of wearable microsensors to quantify sport-specific movements. Sports medicine. 2015;45:1065-81.
  16. Roetenberg D, Luinge H, Slycke P. Xsens MVN: Full 6DOF human motion tracking using miniature inertial sensors. Xsens Motion Technologies BV, Tech Rep. 2009;1:1-7.
  17. Sgrò F, Coppola R, Pignato S, Lipoma M. A systematic review of the use of technologies for the assessment of movement in physical education. Italian Journal of Educational Technology. 2019;27(1):19-35.
  18. Clark CCT, Bisi MC, Duncan MJ, Stagni R. Technology-based methods for the assessment of fine and gross motor skill in children: A systematic overview of available solutions and future steps for effective in-field use. Journal of Sports Sciences. 2021;39:1236-76.
  19. Bisi MC, Tamburini P, Stagni R. A ‘Fingerprint’ of locomotor maturation: Motor development descriptors, reference development bands and data-set. Gait and Posture. 2019;68:232-7.
  20. Masci I, Vannozzi G, Bergamini E, Pesce C, Getchell N, Cappozzo A. Assessing locomotor skills development in childhood using wearable inertial sensor devices: The running paradigm. Gait and Posture. 2013;37:570-4.
  21. Masci I, Vannozzi G, Getchell N, Cappozzo A. Assessing hopping developmental level in childhood using wearable inertial sensor devices. Motor Control. 2012;16:317-28.
  22. Sgrò F, Mango P, Pignato S, Schembri R, Licari D, Lipoma M. Assessing Standing Long Jump Developmental Levels Using an Inertial Measurement Unit. Perceptual and Motor Skills. 2017;124:21-38.
  23. Grimpampi E, Masci I, Pesce C, Vannozzi G. Quantitative assessment of developmental levels in overarm throwing using wearable inertial sensing technology. Journal of Sports Sciences. 2016;34:1759-65.
  24. Barnes CM, Clark CCT, Rees P, Stratton G, Summers HD. Objective profiling of varied human motion based on normative assessment of magnetometer time series data. Physiological Measurement. 2018;39.
  25. Lander N, Nahavandi D, Mohamed S, Essiet I, Barnett LM. Bringing objectivity to motor skill assessment in children. Journal of Sports Sciences. 2020;38:1539-49.
  26. Barnett LM, van Beurden E, Morgan PJ, Brooks LO, Beard JR. Childhood Motor Skill Proficiency as a Predictor of Adolescent Physical Activity. Journal of Adolescent Health. 2009;44:252-9.
  27. Lai SK, Costigan SA, Morgan PJ, Lubans DR, Stodden DF, Salmon J, et al. Do school-based interventions focusing on physical activity, fitness, or fundamental movement skill competency produce a sustained impact in these outcomes in children and adolescents? A systematic review of follow-up studies. Sports Medicine. 2014;44:67-79.
  28. Wagner H, Pfusterschmied J, von Duvillard SP, Müller E. Performance and kinematics of various throwing techniques in team-handball. Journal of sports science & medicine. 2011;10(1): 73-80
  29. O'keeffe S, Harrison A, Smyth P. Transfer or specificity? An applied investigation into the relationship between fundamental overarm throwing and related sport skills. Physical Education and Sport Pedagogy. 2007;12(2):89-102.
  30. Sarmad z, Bazargan a, Hejazi e. Research methods in behavioral sciences. tehran: Aghah Publication Institute; 2004. 405 p.
  31. Sharifi hp, Sharifi n. research methods in behavioral sciences tehran: sokhan; 2004. 448 p.
  32. Takizade K, Farsi A, Baghernia R, Abdoli B, Asle Mohammadizade M. Validity and reliability of a Persian version of developmental coordination disorder questionnaire in 3-5 aged children. Journal of Research in Rehabilitation Sciences. 2013;9(3):502-14.
  33. Camomilla V, Bergamini E, Fantozzi S, Vannozzi G. Trends supporting the in-field use of wearable inertial sensors for sport performance evaluation: A systematic review. Sensors. 2018;18(3):873.
  34. Ulrich DA. The test of gross motor development-3 (TGMD-3): Administration, scoring, and international norms. Spor Bilimleri Dergisi. 2013;24(2):27-33.
  35. Mohammadi F, Bahram A, Khalaji H, Ghadiri F. The Validity and Reliability of Test of Gross Motor Development – 3rd Edition among 3-10 Years Old Children in Ahvaz. Jundishapur Scientific Medical Journal. 2017;16(4):379-91.
  36. Mostafavi R, Ziaee V, Akbari H, Haji-Hosseini S. The effects of spark physical education program on fundamental motor skills in 4-6 year-old children. Iranian journal of pediatrics. 2013;23(2):216.
  37. Goodway JD, Ozmun JC, Gallahue DL. Understanding motor development: Infants, children, adolescents, adults: Jones & Bartlett Learning; 2019.424 p.
  38. Mousavi Hondori H, Khademi M. A review on technical and clinical impact of microsoft kinect on physical therapy and rehabilitation. Journal of medical engineering. 2014;2014.
  39. Bisi MC, Stagni R. Complexity of human gait pattern at different ages assessed using multiscale entropy: From development to decline. Gait and Posture. 2016;47:37-42.
  40. Bisi MC, Panebianco GP, Polman R, Stagni R. Objective assessment of movement competence in children using wearable sensors: An instrumented version of the TGMD-2 locomotor subtest. Gait & Posture. 2017;56:42-8.

 

Journal of Advanced Sport Technology
Volume 6, Issue 2 - Serial Number 11
November 2022
Pages 99-111

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History

  • Receive Date: 07 October 2022
  • Revise Date: 17 November 2022
  • Accept Date: 27 December 2022

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