Artificial Intelligence Approach in Biomechanical Analysis of Gait.

سال انتشار: 1402
محل انتشار: دوفصلنامه فناوری های پیشرفته ورزشی، دوره: 7، شماره: 2
کد COI اختصاصی: JR_JJAST-7-2_003
زبان مقاله: انگلیسی
تعداد مشاهده: 169

نویسندگان

Department of Sports Biomechanics, Central Tehran Branch, Islamic Azad University, Tehran

Department of Sports Biomechanics, Faculty of Physical Education and Sports Science, Islamic Azad University of Central Tehran Branch, Tehran, Iran.

Hamed Abbasi

Department of Sport Injuries and Corrective Exercises, Sports Medicine Research Center, Sport Sciences Research Institute, Tehran, Iran

Davood Khezri

Department 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 ۱۰-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 ۱۰۰BPM. 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 ۲۶ (p< ۰.۰۵). 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.

کلیدواژه ها

Artificial Intelligence, Gait, Biomechanics, Machine Learning, Clustering

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.