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Takizadeh, Khaled, Bagherzadeh, Fazlollah, Sheikh, Mahmoud, Houminiyan Sharif Abadi, Davoud, Veisi, Hadi. (1402). The Application of Artificial Neural Network and Wearable Inertial Sensor in Kicking Skill Assessment.. سامانه مدیریت نشریات علمی, 8(1), 34-45. doi: 10.22098/jast.2024.2978
Khaled Takizadeh; Fazlollah Bagherzadeh; Mahmoud Sheikh; Davoud Houminiyan Sharif Abadi; Hadi Veisi. "The Application of Artificial Neural Network and Wearable Inertial Sensor in Kicking Skill Assessment.". سامانه مدیریت نشریات علمی, 8, 1, 1402, 34-45. doi: 10.22098/jast.2024.2978
Takizadeh, Khaled, Bagherzadeh, Fazlollah, Sheikh, Mahmoud, Houminiyan Sharif Abadi, Davoud, Veisi, Hadi. (1402). 'The Application of Artificial Neural Network and Wearable Inertial Sensor in Kicking Skill Assessment.', سامانه مدیریت نشریات علمی, 8(1), pp. 34-45. doi: 10.22098/jast.2024.2978
Takizadeh, Khaled, Bagherzadeh, Fazlollah, Sheikh, Mahmoud, Houminiyan Sharif Abadi, Davoud, Veisi, Hadi. The Application of Artificial Neural Network and Wearable Inertial Sensor in Kicking Skill Assessment.. سامانه مدیریت نشریات علمی, 1402; 8(1): 34-45. doi: 10.22098/jast.2024.2978

The Application of Artificial Neural Network and Wearable Inertial Sensor in Kicking Skill Assessment.

Journal of Advanced Sport Technology
دوره 8، شماره 1 - شماره پیاپی 16، خرداد 2024، صفحه 34-45    اصل مقاله (541.8 K)
نوع مقاله: Original research papers
شناسه دیجیتال (DOI): 10.22098/jast.2024.2978
نویسندگان
Khaled Takizadeh1؛ Fazlollah Bagherzadeh* 2؛ Mahmoud Sheikh2؛ Davoud Houminiyan Sharif Abadi3؛ Hadi Veisi4
1Department of Motor Behavior, Faculty of Physical Education and Sport Sciences, University of Tehran, Tehran, Iran
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
The trade-off between speed and accuracy in process-oriented tests of fundamental motor skills development has always been a challenge in motor development screening plans. Thus, this study was designed to evaluate the feasibility of using wearable inertial sensors (IMUs) based on artificial intelligence algorithms to assess kicking skill. Thirteen children aged 4 to 10 years (age = 8±1.37) (boys = 58%) participated in this study. The subjects were asked to do at least ten repetitions of the kicking skill according to the TGMD-3. Trials were captured with video recording and three wearable inertial sensors installed on the ankles and lower back. K-Nearest Neighbor artificial intelligence algorithms automatically classified the linear acceleration and angular velocity 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 kicking was 95%, ICC =0.90 (CI=0.86-0.95). The scoring time was reduced from 5 minutes per trial (in an expert-oriented way) to less than 30 seconds (using artificial intelligence). As a result, this method was a reliable and practical way to assess the fundamental motor skills. Also, by maintaining relative accuracy, it was possible to reduce test time for research, clinical, sports, and educational purposes.
کلیدواژه‌ها
TGMD3؛ Wearable Inertial Measurement Unit؛ Artificial Intelligence؛ Motor Development؛ Automatic Assessment
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