آمار

تعداد نشریات30
تعداد شماره‌ها425
تعداد مقالات3,743
تعداد مشاهده مقاله5,869,040
تعداد دریافت فایل اصل مقاله4,058,785
Hajihosseini, Samaneh, Sheikh, Mahmoud, Ghaiur Najafabadi, Mahboubeh, Houminiyan Sharif Abadi, Davoud, Veisi, Hadi. (1401). Wearable Measurement Unit for Objective Assessment of Catching and Underhand Throwing Development. سامانه مدیریت نشریات علمی, 7(1), 33-44. doi: 10.22098/jast.2023.2275
Samaneh Hajihosseini; Mahmoud Sheikh; Mahboubeh Ghaiur Najafabadi; Davoud Houminiyan Sharif Abadi; Hadi Veisi. "Wearable Measurement Unit for Objective Assessment of Catching and Underhand Throwing Development". سامانه مدیریت نشریات علمی, 7, 1, 1401, 33-44. doi: 10.22098/jast.2023.2275
Hajihosseini, Samaneh, Sheikh, Mahmoud, Ghaiur Najafabadi, Mahboubeh, Houminiyan Sharif Abadi, Davoud, Veisi, Hadi. (1401). 'Wearable Measurement Unit for Objective Assessment of Catching and Underhand Throwing Development', سامانه مدیریت نشریات علمی, 7(1), pp. 33-44. doi: 10.22098/jast.2023.2275
Hajihosseini, Samaneh, Sheikh, Mahmoud, Ghaiur Najafabadi, Mahboubeh, Houminiyan Sharif Abadi, Davoud, Veisi, Hadi. Wearable Measurement Unit for Objective Assessment of Catching and Underhand Throwing Development. سامانه مدیریت نشریات علمی, 1401; 7(1): 33-44. doi: 10.22098/jast.2023.2275

Wearable Measurement Unit for Objective Assessment of Catching and Underhand Throwing Development

Journal of Advanced Sport Technology
دوره 7، شماره 1 - شماره پیاپی 12، خرداد 2023، صفحه 33-44    اصل مقاله (608.46 K)
نوع مقاله: Original research papers
شناسه دیجیتال (DOI): 10.22098/jast.2023.2275
نویسندگان
Samaneh Hajihosseini1؛ Mahmoud Sheikh* 2؛ Mahboubeh Ghaiur Najafabadi3؛ Davoud Houminiyan Sharif Abadi3؛ Hadi Veisi4
1Department of Physical Education, Gorgan University of Agricultural Sciences and Natural Resources
2Department of motor behavior, Faculty of Physical Education and Sport Sciences, University of Tehran, Tehran, Iran
3Department of motor behavior, Faculty of Physical Education and Sport Sciences, University of Tehran, Tehran, Iran.
4Department of Interdisciplinary Technology /Network Science and Technology, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.
چکیده
Developing fundamental movement skills (FMS) as the building blocks of complex sports skills and daily physical activity is crucial. The mechanically optimal performance can be determined by qualitative changes in the sensitive aspects of the skill. Accurate scoring of this process is time-consuming and requires minimum training and experience. Thus, this study was designed to evaluate the feasibility of using wearable inertial units (IMUs) based on artificial intelligence algorithms (AIA) for objective assessment of catching and throwing skills.Thirteen children aged 4 to 10 years (age = 7±1.84) (boys = 53%) were asked to do at least ten repetitions of two hands ball catch and underhand throw according to the Test of Gross Motor Skills Development- third edition (TGMD-3). Trials were captured with video recording and three IMUs, simultaneously. Dynamic Time Warping (DTW) and K-Nearest Neighbor (KNN) artificial intelligence algorithms automatically classified IMU signals. The intraclass correlation coefficient (ICC) was calculated between expert scores and the artificial intelligence algorithm. All tests were done at a 95% confidence interval. The classification accuracy of the KNN algorithm (k=7) for two hand catch was 73%, ICC =0.51 (CI=0.25-0.69), and for underhand throw was 70%, ICC= 0.559, (CI=0.314-0.717). The algorithm accuracy when using lower back sensor data was 72% for the tow-hand catch and 78% for the underhand throw. The scoring time was reduced from 5 minutes per skill (in an expert-oriented way) to less than 30 seconds (using artificial intelligence). A close examination of the artificial intelligence classification revealed several aspects of performance that did not play an influential role in trials but were artificially consistent with the TGMD-3. Locating the sensor in the waist area for these two skills will save the cost and time in screening plans. This instrument assessment provides instant feedback, is portable, economical and easy-to-use, and is suitable for educational setting. In the future, more research should be conducted on IMUs' real-world applications by teachers, researchers, clinicians, and coaches.
کلیدواژه‌ها
Test of Gross Motor Development؛ Wearable Inertial Measurement Unit؛ Artificial Intelligence Algorithms؛ Fundamental Movement Skills؛ Motor Development Assessment
مراجع
  1. Clark JE, Metcalfe JS. The mountain of motor development: A metaphor. Motor development: Research and reviews. 2002;2(163-190):183-202.
  2. Holfelder B, Schott N. Relationship of fundamental movement skills and physical activity in children and adolescents: A systematic review. Psychology of sport and exercise. 2014;15(4):382-91.
  3. Logan SW, Kipling Webster E, Getchell N, Pfeiffer KA, Robinson LE. Relationship between fundamental motor skill competence and physical activity during childhood and adolescence: A systematic review. Kinesiology Review. 2015;4(4):416-26.
  4. Williams MD, Hammond AM, Moran J. Youth basketball coaches’ perceptions and implementation of fundamental movement skills training: toward a realist evaluation. Journal of Teaching in Physical Education. 2021;1(aop):1-8.
  5. Yan JH, Jevas S. Young girls' developmental skills in underarm throwing. Perceptual and motor skills. 2004;99(1):39-47.
  6. Sekaran SN, Reid SL, Chin AW, Ndiaye S, Licari MK. Catch! Movement kinematics of two-handed catching in boys with developmental coordination disorder. Gait & posture. 2012;36(1):27-32.
  7. Przysucha EP, Maraj BK. Movement coordination in ball catching: comparison between boys with and without developmental coordination disorder. Research Quarterly for Exercise and Sport. 2010;81(2):152-61.
  8. Godfrey A, Lara J, Munro C, Wiuff C, Chowdhury S, Del Din S, et al. Instrumented assessment of test battery for physical capability using an accelerometer: a feasibility study. Physiological measurement. 2015;36(5):N71-N83.
  9. Bisi MC, Stagni R. Complexity of human gait pattern at different ages assessed using multiscale entropy: From development to decline. Gait & Posture. 2016;47:37-42.
  10. Ricci M, Terribili M, Giannini F, Errico V, Pallotti A, Galasso C, et al. Wearable-based electronics to objectively support diagnosis of motor impairments in school-aged children. Journal of Biomechanics. 2019;83:243-52.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. Hajihosseini S, Sheikh M, GHaiur Najafabadi M, Houminiyan Sharif Abadi D, Veisi H, Bagherzadeh F. Automated Assessment of Developmental Stage of Overhand Throwing Skill. Journal of Advanced Sport Technology. 2022;6(2):99-111.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. Balali M, Parvinpour S, Shafizadeh M. Effect of motor development levels on kinematic synergies during two-hand catching in children. Motor Control. 2020;24(4):543-57.
  21. 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; Volume 2014, Article ID 846514, 1-16.
  22. Pirsiavash H, Vondrick C, Torralba A, editors. Assessing the quality of actions. European Conference on Computer Vision; 2014: Proceedings, Part VI 13 2014 : 556-571.
  23. Oliver GD, Dwelly PM, Kwon Y-H. Kinematic motion of the windmill softball pitch in prepubescent and pubescent girls. The Journal of Strength & Conditioning Research. 2010;24(9):2400-7.
  24. sarmad z, bazargan a, hejazi e. Research methods in behavioral sciences. tehran: Aghah Publication Institute; 2004. 405 p.
  25. sharifi hp, sharifi n. research methods in behavioral sciences tehran: sokhan; 2004. 448 p.
  26. 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.
  27. 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):873-923.
  28. Ulrich DA. The test of gross motor development-3 (TGMD-3): Administration, scoring, and international norms. Spor Bilimleri Dergisi. 2013;24(2):27-33.
  29. 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):216-219.
  30. 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.
  31. Worsey MT, Pahl R, Espinosa HG, Shepherd JB, Thiel DV. Is machine learning and automatic classification of swimming data what unlocks the power of inertial measurement units in swimming? Journal of Sports Sciences. 2021;39(18):2095-114.
  32. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of chiropractic medicine. 2016 Jun 1;15(2):155-63.
  33. Griffiths A, Toovey R, Morgan PE, Spittle AJ. Psychometric properties of gross motor assessment tools for children: A systematic review. BMJ Open. 2018;8. e021734.1-14
  34. Gallahue DL, Ozmun JC, Goodway J. Understanding motor development: Infants, children, adolescents, adults: Boston; 2006.424p.
  35. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015;521(7553):436.
  36. Hammerla NY, Halloran S, Plötz T. Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv preprint arXiv:160408880. 2016.1-7
  37. Zeng M, Nguyen LT, Yu B, Mengshoel OJ, Zhu J, Wu P, et al., editors. Convolutional neural networks for human activity recognition using mobile sensors. 6th International Conference on Mobile Computing, Applications and Services; 2014: IEEE.1-9
 

 

آمار
تعداد مشاهده مقاله: 393
تعداد دریافت فایل اصل مقاله: 344


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب