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Molavian, Rozhin, Fatahi, Ali, Abbasi, Hamed, Khezri, Davood. (1402). Artificial Intelligence Approach in Biomechanical Analysis of Gait.. سامانه مدیریت نشریات علمی, 7(2), 23-37. doi: 10.22098/jast.2023.2386
Rozhin Molavian; Ali Fatahi; Hamed Abbasi; Davood Khezri. "Artificial Intelligence Approach in Biomechanical Analysis of Gait.". سامانه مدیریت نشریات علمی, 7, 2, 1402, 23-37. doi: 10.22098/jast.2023.2386
Molavian, Rozhin, Fatahi, Ali, Abbasi, Hamed, Khezri, Davood. (1402). 'Artificial Intelligence Approach in Biomechanical Analysis of Gait.', سامانه مدیریت نشریات علمی, 7(2), pp. 23-37. doi: 10.22098/jast.2023.2386
Molavian, Rozhin, Fatahi, Ali, Abbasi, Hamed, Khezri, Davood. Artificial Intelligence Approach in Biomechanical Analysis of Gait.. سامانه مدیریت نشریات علمی, 1402; 7(2): 23-37. doi: 10.22098/jast.2023.2386

Artificial Intelligence Approach in Biomechanical Analysis of Gait.

Journal of Advanced Sport Technology
دوره 7، شماره 2 - شماره پیاپی 13، شهریور 2023، صفحه 23-37    اصل مقاله (657.24 K)
نوع مقاله: Original research papers
شناسه دیجیتال (DOI): 10.22098/jast.2023.2386
نویسندگان
Rozhin Molavian1؛ Ali Fatahi* 2؛ Hamed Abbasi3؛ Davood Khezri4
1Department of Sports Biomechanics, Central Tehran Branch, Islamic Azad University, Tehran
2Department of Sports Biomechanics, Faculty of Physical Education and Sports Science, Islamic Azad University of Central Tehran Branch, Tehran, Iran.
3Department of Sport Injuries and Corrective Exercises, Sports Medicine Research Center, Sport Sciences Research Institute, Tehran, Iran
4Department of Sports Biomechanics, Sport Sciences Research Institute, Tehran, Iran.
چکیده
The objective of the current investigation was to conduct a biomechanical analysis of human gait based on the Unsupervised machine learning – Artificial Intelligence approach. Twenty-eight junior active males participated in the study. Following the placement of the markers, the participants were asked to complete the gait task in a 10-meter gateway where the dominant leg contact was placed on the third step and non- non-dominant leg on the fourth step. The task was executed in two separate attempts, first by the preferred speed of the participants and second with a steady speed of 100BPM. The Hierarchical approach consisting of Nearest Neighbor and the utilization of Z score was employed to discern uniform gait biomechanical patterns of the entire participant according to the values of joint angles and joint moments in both conditions - preferred and steady speeds by SPSS software version 26 (p<0.05). Considering a combination of both kinematics and kinetics parameters, in preferred speed, the hip and knee in the vertical direction for both dominant and non-dominant limbs are classified in one cluster, but in a steady speed, the hip in mediolateral direction and knee in the vertical direction for both dominant and non-dominant limbs are presented in one cluster. The kinematic and kinetic variables are useful in gate clustering to categorize gait patterns. These variables can be subdivided into homogeneous subgroups for a more detailed understanding of human locomotion.
کلیدواژه‌ها
Artificial Intelligence؛ Gait؛ Biomechanics؛ Machine learning؛ Clustering
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